CC '21: "PolyBench/Python: Benchmarking ..."
PolyBench/Python: Benchmarking Python Environments with Polyhedral Optimizations
Miguel Á. Abella-González, Pedro Carollo-Fernández, Louis-Noël Pouchet, Fabrice Rastello, and Gabriel Rodríguez
(Universidade da Coruña, Spain; Colorado State University, USA; Inria, France)
Python has become one of the most used and taught languages nowadays. Its expressiveness, cross-compatibility and ease of use have made it popular in areas as diverse as finance, bioinformatics or machine learning. However, Python programs are often significantly slower to execute than an equivalent native C implementation, especially for computation-intensive numerical kernels.
This work presents PolyBench/Python, implementing the 30 kernels in PolyBench/C, one of the standard benchmark suites for polyhedral optimization, in Python. In addition to the benchmark kernels, a functional wrapper including mechanisms for performance measurement, testing, and execution configuration has been developed. The framework includes support for different ways to translate C-array codes into Python, offering insight into the tradeoffs of Python lists and NumPy arrays. The benchmark performance is thoroughly evaluated on different Python interpreters, and compared against its PolyBench/C counterpart to highlight the profitability (or lack thereof) of using Python for regular numerical codes.
@InProceedings{CC21p59,
author = {Miguel Á. Abella-González and Pedro Carollo-Fernández and Louis-Noël Pouchet and Fabrice Rastello and Gabriel Rodríguez},
title = {PolyBench/Python: Benchmarking Python Environments with Polyhedral Optimizations},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {59--70},
doi = {10.1145/3446804.3446842},
year = {2021},
}
Publisher's Version
Artifacts Reusable
CC '21: "Data-Aware Process Networks ..."
Data-Aware Process Networks
Christophe Alias and Alexandru Plesco
(CNRS, France; ENS Lyon, France; Inria, France; University of Lyon, France; XtremLogic, France)
With the emergence of reconfigurable FPGA circuits as a credible alternative to GPUs for HPC acceleration, new compilation paradigms are required to map high-level algorithmic descriptions to a circuit configuration (High-Level Synthesis, HLS). In particular, novel parallelization algorithms and intermediate representations are required. In this paper, we present the data-aware process networks (DPN), a dataflow intermediate representation suitable for HLS in the context of high-performance computing. DPN combines the benefits of a low-level dataflow representation -- close to the final circuit -- and affine iteration space tiling to explore the parallelization trade-offs (local memory size, communication volume, parallelization degree). We outline our compilation algorithms to map a C program to a DPN (front-end), then to map a DPN to an FPGA configuration (back-end). Finally, we present synthesis results on compute-intensive kernels from the Polybench suite.
@InProceedings{CC21p1,
author = {Christophe Alias and Alexandru Plesco},
title = {Data-Aware Process Networks},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {1--11},
doi = {10.1145/3446804.3446847},
year = {2021},
}
Publisher's Version
CC '21: "Lightning BOLT: Powerful, ..."
Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization
Maksim Panchenko, Rafael Auler, Laith Sakka, and Guilherme Ottoni
(Facebook, USA)
Profile-guided binary optimization has proved to be an important technology to achieve peak performance, particularly for large-scale binaries that are typical for data-center applications. By applying the profile data at the same representation where sampling-based profiling is collected, binary optimizers can provide double-digit speedups over binaries compiled with profile-guided optimizations using similarly collected profile data. The main blocker for adoption of binary optimizers in practice is the overhead that they add to the already long and demanding build pipelines used for producing highly optimized binaries, which already include aggressive compiler optimizations guided by profile data and also link-time optimizations. This paper addresses the overheads of binary optimizers in the context of BOLT, a modern and powerful open-source binary optimizer. More specifically, this paper describes Lightning BOLT, which is an improved version of the BOLT binary optimizer that drastically reduces BOLT’s processing time and memory requirements, while preserving BOLT’s effectiveness in improving the final binary’s performance. Using a set of real-world data-center and open-source applications, we show that Lightning BOLT speeds up BOLT’s processing by an average of 4.71× and reduces BOLT’s memory consumption by 70.5% on average. Furthermore, Lightning BOLT also provides an adjustable mechanism to further reduce BOLT’s overheads at the cost of some lost performance for the final binary.
@InProceedings{CC21p119,
author = {Maksim Panchenko and Rafael Auler and Laith Sakka and Guilherme Ottoni},
title = {Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {119--130},
doi = {10.1145/3446804.3446843},
year = {2021},
}
Publisher's Version
Artifacts Functional
Results Reproduced
CC '21: "Resolvable Ambiguity: Principled ..."
Resolvable Ambiguity: Principled Resolution of Syntactically Ambiguous Programs
Viktor Palmkvist, Elias Castegren, Philipp Haller, and David Broman
(KTH, Sweden)
When building a new programming language, it can be useful to compose parts of existing languages to avoid repeating implementation work. However, this is problematic already at the syntax level, as composing the grammars of language fragments can easily lead to an ambiguous grammar. State-of-the-art parser tools cannot handle ambiguity truly well: either the grammar cannot be handled at all, or the tools give little help to an end-user who writes an ambiguous program. This composability problem is twofold: (i) how can we detect if the composed grammar is ambiguous, and (ii) if it is ambiguous, how can we help a user resolve an ambiguous program? In this paper, we depart from the traditional view of unambiguous grammar design and enable a language designer to work with an ambiguous grammar, while giving users the tools needed to handle these ambiguities. We introduce the concept of resolvable ambiguity wherein a user can resolve an ambiguous program by editing it, as well as an approach to computing the resolutions of an ambiguous program. Furthermore, we present a method based on property-based testing to identify if a composed grammar is unambiguous, resolvably ambiguous, or unresolvably ambiguous. The method is implemented in Haskell and evaluated on a large set of language fragments selected from different languages. The evaluation shows that (i) the approach can handle significantly more cases of language compositions compared to approaches which ban ambiguity altogether, and (ii) that the approach is fast enough to be used in practice.
@InProceedings{CC21p153,
author = {Viktor Palmkvist and Elias Castegren and Philipp Haller and David Broman},
title = {Resolvable Ambiguity: Principled Resolution of Syntactically Ambiguous Programs},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {153--164},
doi = {10.1145/3446804.3446846},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced
CC '21: "Compact Native Code Generation ..."
Compact Native Code Generation for Dynamic Languages on Micro-core Architectures
Maurice Jamieson and Nick Brown
(University of Edinburgh, UK)
Micro-core architectures combine many simple, low memory, low power-consuming CPU cores onto a single chip. Potentially providing significant performance and low power consumption, this technology is not only of great interest in embedded, edge, and IoT uses, but also potentially as accelerators for data-center workloads. Due to the restricted nature of such CPUs, these architectures have traditionally been challenging to program, not least due to the very constrained amounts of memory (often around 32KB) and idiosyncrasies of the technology. However, more recently, dynamic languages such as Python have been ported to a number of micro-cores, but these are often delivered as interpreters which have an associated performance limitation.
Targeting the four objectives of performance, unlimited code-size, portability between architectures, and maintaining the programmer productivity benefits of dynamic languages, the limited memory available means that classic techniques employed by dynamic language compilers, such as just-in-time (JIT), are simply not feasible. In this paper we describe the construction of a compilation approach for dynamic languages on micro-core architectures which aims to meet these four objectives, and use Python as a vehicle for exploring the application of this in replacing the existing micro-core interpreter. Our experiments focus on the metrics of performance, architecture portability, minimum memory size, and programmer productivity, comparing our approach against that of writing native C code. The outcome of this work is the identification of a series of techniques that are not only suitable for compiling Python code, but also applicable to a wide variety of dynamic languages on micro-cores.
@InProceedings{CC21p131,
author = {Maurice Jamieson and Nick Brown},
title = {Compact Native Code Generation for Dynamic Languages on Micro-core Architectures},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {131--140},
doi = {10.1145/3446804.3446853},
year = {2021},
}
Publisher's Version
CC '21: "PolyBench/Python: Benchmarking ..."
PolyBench/Python: Benchmarking Python Environments with Polyhedral Optimizations
Miguel Á. Abella-González, Pedro Carollo-Fernández, Louis-Noël Pouchet, Fabrice Rastello, and Gabriel Rodríguez
(Universidade da Coruña, Spain; Colorado State University, USA; Inria, France)
Python has become one of the most used and taught languages nowadays. Its expressiveness, cross-compatibility and ease of use have made it popular in areas as diverse as finance, bioinformatics or machine learning. However, Python programs are often significantly slower to execute than an equivalent native C implementation, especially for computation-intensive numerical kernels.
This work presents PolyBench/Python, implementing the 30 kernels in PolyBench/C, one of the standard benchmark suites for polyhedral optimization, in Python. In addition to the benchmark kernels, a functional wrapper including mechanisms for performance measurement, testing, and execution configuration has been developed. The framework includes support for different ways to translate C-array codes into Python, offering insight into the tradeoffs of Python lists and NumPy arrays. The benchmark performance is thoroughly evaluated on different Python interpreters, and compared against its PolyBench/C counterpart to highlight the profitability (or lack thereof) of using Python for regular numerical codes.
@InProceedings{CC21p59,
author = {Miguel Á. Abella-González and Pedro Carollo-Fernández and Louis-Noël Pouchet and Fabrice Rastello and Gabriel Rodríguez},
title = {PolyBench/Python: Benchmarking Python Environments with Polyhedral Optimizations},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {59--70},
doi = {10.1145/3446804.3446842},
year = {2021},
}
Publisher's Version
Artifacts Reusable
CC '21: "Resolvable Ambiguity: Principled ..."
Resolvable Ambiguity: Principled Resolution of Syntactically Ambiguous Programs
Viktor Palmkvist, Elias Castegren, Philipp Haller, and David Broman
(KTH, Sweden)
When building a new programming language, it can be useful to compose parts of existing languages to avoid repeating implementation work. However, this is problematic already at the syntax level, as composing the grammars of language fragments can easily lead to an ambiguous grammar. State-of-the-art parser tools cannot handle ambiguity truly well: either the grammar cannot be handled at all, or the tools give little help to an end-user who writes an ambiguous program. This composability problem is twofold: (i) how can we detect if the composed grammar is ambiguous, and (ii) if it is ambiguous, how can we help a user resolve an ambiguous program? In this paper, we depart from the traditional view of unambiguous grammar design and enable a language designer to work with an ambiguous grammar, while giving users the tools needed to handle these ambiguities. We introduce the concept of resolvable ambiguity wherein a user can resolve an ambiguous program by editing it, as well as an approach to computing the resolutions of an ambiguous program. Furthermore, we present a method based on property-based testing to identify if a composed grammar is unambiguous, resolvably ambiguous, or unresolvably ambiguous. The method is implemented in Haskell and evaluated on a large set of language fragments selected from different languages. The evaluation shows that (i) the approach can handle significantly more cases of language compositions compared to approaches which ban ambiguity altogether, and (ii) that the approach is fast enough to be used in practice.
@InProceedings{CC21p153,
author = {Viktor Palmkvist and Elias Castegren and Philipp Haller and David Broman},
title = {Resolvable Ambiguity: Principled Resolution of Syntactically Ambiguous Programs},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {153--164},
doi = {10.1145/3446804.3446846},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced
CC '21: "NSan: A Floating-Point Numerical ..."
NSan: A Floating-Point Numerical Sanitizer
Clement Courbet
(Google Research, France)
Sanitizers are a relatively recent trend in software engineering. They aim at automatically finding bugs in programs, and they are now commonly available to programmers as part of compiler toolchains. For example, the LLVM project includes out-of-the-box sanitizers to detect thread safety (tsan), memory (asan,msan,lsan), or undefined behaviour (ubsan) bugs.
In this article, we present nsan, a new sanitizer for locating and debugging floating-point numerical issues, implemented inside the LLVM sanitizer framework. nsan puts emphasis on practicality. It aims at providing precise, and actionable feedback, in a timely manner.
nsan uses compile-time instrumentation to augment each floating-point computation in the program with a higher-precision shadow which is checked for consistency during program execution. This makes nsan between 1 and 4 orders of magnitude faster than existing approaches, which allows running it routinely as part of unit tests, or detecting issues in large production applications.
@InProceedings{CC21p83,
author = {Clement Courbet},
title = {NSan: A Floating-Point Numerical Sanitizer},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {83--93},
doi = {10.1145/3446804.3446848},
year = {2021},
}
Publisher's Version
CC '21: "Exploring the Space of Optimization ..."
Exploring the Space of Optimization Sequences for Code-Size Reduction: Insights and Tools
Anderson Faustino da Silva, Bernardo N. B. de Lima, and Fernando Magno Quintão Pereira
(State University of Maringá, Brazil; Federal University of Minas Gerais, Brazil)
The optimization space of a compiler is the set of every possible sequence of optimizations that said compiler can use. The exploration of the optimization space of any mainstream compiler has been, for decades, hampered by the lack of benchmarks. However, recent efforts from different research groups have made available a large quantity of compilable code that can be, today, used to overcome this problem. In this paper, we use 15,000 programs from a public collection to explore the optimization space of LLVM, focusing on code-size reduction. This exploration reveals that the probability of beating the default optimization levels of LLVM with random sequences ranges from 10% (considering opt -Oz) to 19% (considering clang -Os). Yet, the distribution of probabilities is uneven across programs: the default levels work well for most programs, and poorly for a few. Based on these observations, we introduce the notion of an Optimization Cache, a table of programs to optimization sequences that can be used to support predictive compilation. We then use an optimization cache to build what we call a Default Covering Set: a small ensemble of optimization sequences that, once combined, tend to be good for any program. Optimization caches and default covering sets are used independently. The former, when applied onto MiBench, yield programs that are 11.9% smaller than programs produced by opt -Os, on average. The latter produce programs 12.5% smaller.
@InProceedings{CC21p47,
author = {Anderson Faustino da Silva and Bernardo N. B. de Lima and Fernando Magno Quintão Pereira},
title = {Exploring the Space of Optimization Sequences for Code-Size Reduction: Insights and Tools},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {47--58},
doi = {10.1145/3446804.3446849},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced
CC '21: "Communication-Safe Web Programming ..."
Communication-Safe Web Programming in TypeScript with Routed Multiparty Session Types
Anson Miu, Francisco Ferreira, Nobuko Yoshida, and Fangyi Zhou
(Imperial College London, UK; Bloomberg, UK)
Modern web programming involves coordinating interactions between browser clients and a server. Typically, the interactions in web-based distributed systems are informally described, making it hard to ensure correctness, especially communication safety, i.e. all endpoints progress without type errors or deadlocks, conforming to a specified protocol.
We present STScript, a toolchain that generates TypeScript APIs for communication-safe web development over WebSockets, and RouST, a new session type theory that supports multiparty communications with routing mechanisms. STScript provides developers with TypeScript APIs generated from a communication protocol specification based on RouST. The generated APIs build upon TypeScript concurrency practices, complement the event-driven style of programming in full-stack web development, and are compatible with the Node.js runtime for server-side endpoints and the React.js framework for browser-side endpoints.
RouST can express multiparty interactions routed via an intermediate participant. It supports peer-to-peer communication between browser-side endpoints by routing communication via the server in a way that avoids excessive serialisation. RouST guarantees communication safety for endpoint web applications written using STScript APIs.
We evaluate the expressiveness of STScript for modern web programming using several production-ready case studies deployed as web applications.
@InProceedings{CC21p94,
author = {Anson Miu and Francisco Ferreira and Nobuko Yoshida and Fangyi Zhou},
title = {Communication-Safe Web Programming in TypeScript with Routed Multiparty Session Types},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {94--106},
doi = {10.1145/3446804.3446854},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced
CC '21: "Compiling Data-Parallel Datalog ..."
Compiling Data-Parallel Datalog
Thomas Gilray, Sidharth Kumar, and Kristopher Micinski
(University of Alabama at Birmingham, USA; Syracuse University, USA)
Datalog allows intuitive declarative specification of logical inference tasks while enjoying efficient implementation via state-of-the-art engines such as LogicBlox and Soufflé. These engines enable high-performance implementation of complex logical tasks including graph mining, program analysis, and business analytics. However, all efficient modern Datalog solvers make use of shared memory, and present inherent challenges scalability. In this paper, we leverage recent insights in parallel relational algebra and present a methodology for constructing data-parallel deductive databases. Our approach leverages recent developments in parallelizing relational algebra to create an efficient data-parallel semantics for Datalog. Based on our methodology, we have implemented the first MPI-based data-parallel Datalog solver. Our experiments demonstrate comparable performance and improved single-node scalability versus Soufflé, a state-of-art solver.
@InProceedings{CC21p23,
author = {Thomas Gilray and Sidharth Kumar and Kristopher Micinski},
title = {Compiling Data-Parallel Datalog},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {23--35},
doi = {10.1145/3446804.3446855},
year = {2021},
}
Publisher's Version
CC '21: "Deep NLP-Based Co-evolvement ..."
Deep NLP-Based Co-evolvement for Synthesizing Code Analysis from Natural Language
Zifan Nan, Hui Guan, Xipeng Shen, and Chunhua Liao
(North Carolina State University, USA; University of Massachusetts at Amherst, USA; Lawrence Livermore National Laboratory, USA)
This paper presents Deepsy, a Natural Language-based synthesizer to assist source code analysis. It takes English descriptions of to-be-found code patterns as its inputs, and automatically produces ASTMatcher expressions that are directly usable by LLVM/Clang to materialize intended code analysis. The code analysis domain features profuse complexities in data types and operations, which make it elusive for prior rule-based synthesizers to tackle. On the other hand, machine learning-based solutions are neither applicable due to the scarcity of well labeled examples. This paper presents how Deepsy addresses the challenges by leveraging deep Natural Language Processing (NLP) and creating a new technique named dependency tree-based co-evolvement.Deepsy features an effective design that seamlessly integrates Natural Language dependency analysis into code analysis and meanwhile synergizes it with type-based narrowing and domain-specific guidance. Deepsy achieves over 70.0% expression-level accuracy and 85.1% individual API-level accuracy, significantly outperforming previous solutions.
@InProceedings{CC21p141,
author = {Zifan Nan and Hui Guan and Xipeng Shen and Chunhua Liao},
title = {Deep NLP-Based Co-evolvement for Synthesizing Code Analysis from Natural Language},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {141--152},
doi = {10.1145/3446804.3446852},
year = {2021},
}
Publisher's Version
CC '21: "Resolvable Ambiguity: Principled ..."
Resolvable Ambiguity: Principled Resolution of Syntactically Ambiguous Programs
Viktor Palmkvist, Elias Castegren, Philipp Haller, and David Broman
(KTH, Sweden)
When building a new programming language, it can be useful to compose parts of existing languages to avoid repeating implementation work. However, this is problematic already at the syntax level, as composing the grammars of language fragments can easily lead to an ambiguous grammar. State-of-the-art parser tools cannot handle ambiguity truly well: either the grammar cannot be handled at all, or the tools give little help to an end-user who writes an ambiguous program. This composability problem is twofold: (i) how can we detect if the composed grammar is ambiguous, and (ii) if it is ambiguous, how can we help a user resolve an ambiguous program? In this paper, we depart from the traditional view of unambiguous grammar design and enable a language designer to work with an ambiguous grammar, while giving users the tools needed to handle these ambiguities. We introduce the concept of resolvable ambiguity wherein a user can resolve an ambiguous program by editing it, as well as an approach to computing the resolutions of an ambiguous program. Furthermore, we present a method based on property-based testing to identify if a composed grammar is unambiguous, resolvably ambiguous, or unresolvably ambiguous. The method is implemented in Haskell and evaluated on a large set of language fragments selected from different languages. The evaluation shows that (i) the approach can handle significantly more cases of language compositions compared to approaches which ban ambiguity altogether, and (ii) that the approach is fast enough to be used in practice.
@InProceedings{CC21p153,
author = {Viktor Palmkvist and Elias Castegren and Philipp Haller and David Broman},
title = {Resolvable Ambiguity: Principled Resolution of Syntactically Ambiguous Programs},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {153--164},
doi = {10.1145/3446804.3446846},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced
CC '21: "Compact Native Code Generation ..."
Compact Native Code Generation for Dynamic Languages on Micro-core Architectures
Maurice Jamieson and Nick Brown
(University of Edinburgh, UK)
Micro-core architectures combine many simple, low memory, low power-consuming CPU cores onto a single chip. Potentially providing significant performance and low power consumption, this technology is not only of great interest in embedded, edge, and IoT uses, but also potentially as accelerators for data-center workloads. Due to the restricted nature of such CPUs, these architectures have traditionally been challenging to program, not least due to the very constrained amounts of memory (often around 32KB) and idiosyncrasies of the technology. However, more recently, dynamic languages such as Python have been ported to a number of micro-cores, but these are often delivered as interpreters which have an associated performance limitation.
Targeting the four objectives of performance, unlimited code-size, portability between architectures, and maintaining the programmer productivity benefits of dynamic languages, the limited memory available means that classic techniques employed by dynamic language compilers, such as just-in-time (JIT), are simply not feasible. In this paper we describe the construction of a compilation approach for dynamic languages on micro-core architectures which aims to meet these four objectives, and use Python as a vehicle for exploring the application of this in replacing the existing micro-core interpreter. Our experiments focus on the metrics of performance, architecture portability, minimum memory size, and programmer productivity, comparing our approach against that of writing native C code. The outcome of this work is the identification of a series of techniques that are not only suitable for compiling Python code, but also applicable to a wide variety of dynamic languages on micro-cores.
@InProceedings{CC21p131,
author = {Maurice Jamieson and Nick Brown},
title = {Compact Native Code Generation for Dynamic Languages on Micro-core Architectures},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {131--140},
doi = {10.1145/3446804.3446853},
year = {2021},
}
Publisher's Version
CC '21: "Compiling Data-Parallel Datalog ..."
Compiling Data-Parallel Datalog
Thomas Gilray, Sidharth Kumar, and Kristopher Micinski
(University of Alabama at Birmingham, USA; Syracuse University, USA)
Datalog allows intuitive declarative specification of logical inference tasks while enjoying efficient implementation via state-of-the-art engines such as LogicBlox and Soufflé. These engines enable high-performance implementation of complex logical tasks including graph mining, program analysis, and business analytics. However, all efficient modern Datalog solvers make use of shared memory, and present inherent challenges scalability. In this paper, we leverage recent insights in parallel relational algebra and present a methodology for constructing data-parallel deductive databases. Our approach leverages recent developments in parallelizing relational algebra to create an efficient data-parallel semantics for Datalog. Based on our methodology, we have implemented the first MPI-based data-parallel Datalog solver. Our experiments demonstrate comparable performance and improved single-node scalability versus Soufflé, a state-of-art solver.
@InProceedings{CC21p23,
author = {Thomas Gilray and Sidharth Kumar and Kristopher Micinski},
title = {Compiling Data-Parallel Datalog},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {23--35},
doi = {10.1145/3446804.3446855},
year = {2021},
}
Publisher's Version
CC '21: "Deep NLP-Based Co-evolvement ..."
Deep NLP-Based Co-evolvement for Synthesizing Code Analysis from Natural Language
Zifan Nan, Hui Guan, Xipeng Shen, and Chunhua Liao
(North Carolina State University, USA; University of Massachusetts at Amherst, USA; Lawrence Livermore National Laboratory, USA)
This paper presents Deepsy, a Natural Language-based synthesizer to assist source code analysis. It takes English descriptions of to-be-found code patterns as its inputs, and automatically produces ASTMatcher expressions that are directly usable by LLVM/Clang to materialize intended code analysis. The code analysis domain features profuse complexities in data types and operations, which make it elusive for prior rule-based synthesizers to tackle. On the other hand, machine learning-based solutions are neither applicable due to the scarcity of well labeled examples. This paper presents how Deepsy addresses the challenges by leveraging deep Natural Language Processing (NLP) and creating a new technique named dependency tree-based co-evolvement.Deepsy features an effective design that seamlessly integrates Natural Language dependency analysis into code analysis and meanwhile synergizes it with type-based narrowing and domain-specific guidance. Deepsy achieves over 70.0% expression-level accuracy and 85.1% individual API-level accuracy, significantly outperforming previous solutions.
@InProceedings{CC21p141,
author = {Zifan Nan and Hui Guan and Xipeng Shen and Chunhua Liao},
title = {Deep NLP-Based Co-evolvement for Synthesizing Code Analysis from Natural Language},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {141--152},
doi = {10.1145/3446804.3446852},
year = {2021},
}
Publisher's Version
CC '21: "Integrating a Functional Pattern-Based ..."
Integrating a Functional Pattern-Based IR into MLIR
Martin Lücke, Michel Steuwer, and Aaron Smith
(University of Edinburgh, UK; Microsoft, USA)
The continued specialization in hardware and software due to the end of Moore's law forces us to question fundamental design choices in compilers, and in particular for domain specific languages. The days where a single universal compiler intermediate representation (IR) was sufficient to perform all important optimizations are over. We need novel IRs and ways for them to interact with one another while leveraging established compiler infrastructures.
In this paper, we present a practical implementation of a functional pattern-based IR in the SSA-based MLIR framework. Our IR captures the program semantics as compositions of common computational patterns enabling rewrite-based optimizations. We discuss the integration with other IRs by demonstrating the compilation of a neural network represented as a TensorFlow graph down to optimized LLVM code via our functional pattern-based IR. Our implementation demonstrates for the first time a practical integration of a functional pattern-based IR with other IRs and it enables the construction of sophisticated code generators for domain specific languages.
@InProceedings{CC21p12,
author = {Martin Lücke and Michel Steuwer and Aaron Smith},
title = {Integrating a Functional Pattern-Based IR into MLIR},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {12--22},
doi = {10.1145/3446804.3446844},
year = {2021},
}
Publisher's Version
Artifacts Functional
Results Reproduced
CC '21: "A Modern Compiler for the ..."
A Modern Compiler for the French Tax Code
Denis Merigoux, Raphaël Monat, and Jonathan Protzenko
(Inria, France; Sorbonne University, France; CNRS, France; LIP6, France; Microsoft Research, USA)
In France, income tax is computed from taxpayers' individual returns, using an algorithm that is authored, designed and maintained by the French Public Finances Directorate (DGFiP). This algorithm relies on a legacy custom language and compiler originally designed in 1990, which unlike French wine, did not age well with time. Owing to the shortcomings of the input language and the technical limitations of the compiler, the algorithm is proving harder and harder to maintain, relying on ad-hoc behaviors and workarounds to implement the most recent changes in tax law. Competence loss and aging code also mean that the system does not benefit from any modern compiler techniques that would increase confidence in the implementation.
We overhaul this infrastructure and present Mlang, an open-source compiler toolchain whose goal is to replace the existing infrastructure. Mlang is based on a reverse-engineered formalization of the DGFiP's system, and has been thoroughly validated against the private DGFiP test suite. As such, Mlang has a formal semantics; eliminates previous hand-written workarounds in C; compiles to modern languages (Python); and enables a variety of instrumentations, providing deep insights about the essence of French income tax computation. The DGFiP is now officially transitioning to Mlang for their production system.
@InProceedings{CC21p71,
author = {Denis Merigoux and Raphaël Monat and Jonathan Protzenko},
title = {A Modern Compiler for the French Tax Code},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {71--82},
doi = {10.1145/3446804.3446850},
year = {2021},
}
Publisher's Version
Info
Artifacts Reusable
Results Reproduced
CC '21: "Compiling Data-Parallel Datalog ..."
Compiling Data-Parallel Datalog
Thomas Gilray, Sidharth Kumar, and Kristopher Micinski
(University of Alabama at Birmingham, USA; Syracuse University, USA)
Datalog allows intuitive declarative specification of logical inference tasks while enjoying efficient implementation via state-of-the-art engines such as LogicBlox and Soufflé. These engines enable high-performance implementation of complex logical tasks including graph mining, program analysis, and business analytics. However, all efficient modern Datalog solvers make use of shared memory, and present inherent challenges scalability. In this paper, we leverage recent insights in parallel relational algebra and present a methodology for constructing data-parallel deductive databases. Our approach leverages recent developments in parallelizing relational algebra to create an efficient data-parallel semantics for Datalog. Based on our methodology, we have implemented the first MPI-based data-parallel Datalog solver. Our experiments demonstrate comparable performance and improved single-node scalability versus Soufflé, a state-of-art solver.
@InProceedings{CC21p23,
author = {Thomas Gilray and Sidharth Kumar and Kristopher Micinski},
title = {Compiling Data-Parallel Datalog},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {23--35},
doi = {10.1145/3446804.3446855},
year = {2021},
}
Publisher's Version
CC '21: "Communication-Safe Web Programming ..."
Communication-Safe Web Programming in TypeScript with Routed Multiparty Session Types
Anson Miu, Francisco Ferreira, Nobuko Yoshida, and Fangyi Zhou
(Imperial College London, UK; Bloomberg, UK)
Modern web programming involves coordinating interactions between browser clients and a server. Typically, the interactions in web-based distributed systems are informally described, making it hard to ensure correctness, especially communication safety, i.e. all endpoints progress without type errors or deadlocks, conforming to a specified protocol.
We present STScript, a toolchain that generates TypeScript APIs for communication-safe web development over WebSockets, and RouST, a new session type theory that supports multiparty communications with routing mechanisms. STScript provides developers with TypeScript APIs generated from a communication protocol specification based on RouST. The generated APIs build upon TypeScript concurrency practices, complement the event-driven style of programming in full-stack web development, and are compatible with the Node.js runtime for server-side endpoints and the React.js framework for browser-side endpoints.
RouST can express multiparty interactions routed via an intermediate participant. It supports peer-to-peer communication between browser-side endpoints by routing communication via the server in a way that avoids excessive serialisation. RouST guarantees communication safety for endpoint web applications written using STScript APIs.
We evaluate the expressiveness of STScript for modern web programming using several production-ready case studies deployed as web applications.
@InProceedings{CC21p94,
author = {Anson Miu and Francisco Ferreira and Nobuko Yoshida and Fangyi Zhou},
title = {Communication-Safe Web Programming in TypeScript with Routed Multiparty Session Types},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {94--106},
doi = {10.1145/3446804.3446854},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced
CC '21: "A Modern Compiler for the ..."
A Modern Compiler for the French Tax Code
Denis Merigoux, Raphaël Monat, and Jonathan Protzenko
(Inria, France; Sorbonne University, France; CNRS, France; LIP6, France; Microsoft Research, USA)
In France, income tax is computed from taxpayers' individual returns, using an algorithm that is authored, designed and maintained by the French Public Finances Directorate (DGFiP). This algorithm relies on a legacy custom language and compiler originally designed in 1990, which unlike French wine, did not age well with time. Owing to the shortcomings of the input language and the technical limitations of the compiler, the algorithm is proving harder and harder to maintain, relying on ad-hoc behaviors and workarounds to implement the most recent changes in tax law. Competence loss and aging code also mean that the system does not benefit from any modern compiler techniques that would increase confidence in the implementation.
We overhaul this infrastructure and present Mlang, an open-source compiler toolchain whose goal is to replace the existing infrastructure. Mlang is based on a reverse-engineered formalization of the DGFiP's system, and has been thoroughly validated against the private DGFiP test suite. As such, Mlang has a formal semantics; eliminates previous hand-written workarounds in C; compiles to modern languages (Python); and enables a variety of instrumentations, providing deep insights about the essence of French income tax computation. The DGFiP is now officially transitioning to Mlang for their production system.
@InProceedings{CC21p71,
author = {Denis Merigoux and Raphaël Monat and Jonathan Protzenko},
title = {A Modern Compiler for the French Tax Code},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {71--82},
doi = {10.1145/3446804.3446850},
year = {2021},
}
Publisher's Version
Info
Artifacts Reusable
Results Reproduced
CC '21: "Deep NLP-Based Co-evolvement ..."
Deep NLP-Based Co-evolvement for Synthesizing Code Analysis from Natural Language
Zifan Nan, Hui Guan, Xipeng Shen, and Chunhua Liao
(North Carolina State University, USA; University of Massachusetts at Amherst, USA; Lawrence Livermore National Laboratory, USA)
This paper presents Deepsy, a Natural Language-based synthesizer to assist source code analysis. It takes English descriptions of to-be-found code patterns as its inputs, and automatically produces ASTMatcher expressions that are directly usable by LLVM/Clang to materialize intended code analysis. The code analysis domain features profuse complexities in data types and operations, which make it elusive for prior rule-based synthesizers to tackle. On the other hand, machine learning-based solutions are neither applicable due to the scarcity of well labeled examples. This paper presents how Deepsy addresses the challenges by leveraging deep Natural Language Processing (NLP) and creating a new technique named dependency tree-based co-evolvement.Deepsy features an effective design that seamlessly integrates Natural Language dependency analysis into code analysis and meanwhile synergizes it with type-based narrowing and domain-specific guidance. Deepsy achieves over 70.0% expression-level accuracy and 85.1% individual API-level accuracy, significantly outperforming previous solutions.
@InProceedings{CC21p141,
author = {Zifan Nan and Hui Guan and Xipeng Shen and Chunhua Liao},
title = {Deep NLP-Based Co-evolvement for Synthesizing Code Analysis from Natural Language},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {141--152},
doi = {10.1145/3446804.3446852},
year = {2021},
}
Publisher's Version
CC '21: "Lightning BOLT: Powerful, ..."
Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization
Maksim Panchenko, Rafael Auler, Laith Sakka, and Guilherme Ottoni
(Facebook, USA)
Profile-guided binary optimization has proved to be an important technology to achieve peak performance, particularly for large-scale binaries that are typical for data-center applications. By applying the profile data at the same representation where sampling-based profiling is collected, binary optimizers can provide double-digit speedups over binaries compiled with profile-guided optimizations using similarly collected profile data. The main blocker for adoption of binary optimizers in practice is the overhead that they add to the already long and demanding build pipelines used for producing highly optimized binaries, which already include aggressive compiler optimizations guided by profile data and also link-time optimizations. This paper addresses the overheads of binary optimizers in the context of BOLT, a modern and powerful open-source binary optimizer. More specifically, this paper describes Lightning BOLT, which is an improved version of the BOLT binary optimizer that drastically reduces BOLT’s processing time and memory requirements, while preserving BOLT’s effectiveness in improving the final binary’s performance. Using a set of real-world data-center and open-source applications, we show that Lightning BOLT speeds up BOLT’s processing by an average of 4.71× and reduces BOLT’s memory consumption by 70.5% on average. Furthermore, Lightning BOLT also provides an adjustable mechanism to further reduce BOLT’s overheads at the cost of some lost performance for the final binary.
@InProceedings{CC21p119,
author = {Maksim Panchenko and Rafael Auler and Laith Sakka and Guilherme Ottoni},
title = {Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {119--130},
doi = {10.1145/3446804.3446843},
year = {2021},
}
Publisher's Version
Artifacts Functional
Results Reproduced
CC '21: "Resolvable Ambiguity: Principled ..."
Resolvable Ambiguity: Principled Resolution of Syntactically Ambiguous Programs
Viktor Palmkvist, Elias Castegren, Philipp Haller, and David Broman
(KTH, Sweden)
When building a new programming language, it can be useful to compose parts of existing languages to avoid repeating implementation work. However, this is problematic already at the syntax level, as composing the grammars of language fragments can easily lead to an ambiguous grammar. State-of-the-art parser tools cannot handle ambiguity truly well: either the grammar cannot be handled at all, or the tools give little help to an end-user who writes an ambiguous program. This composability problem is twofold: (i) how can we detect if the composed grammar is ambiguous, and (ii) if it is ambiguous, how can we help a user resolve an ambiguous program? In this paper, we depart from the traditional view of unambiguous grammar design and enable a language designer to work with an ambiguous grammar, while giving users the tools needed to handle these ambiguities. We introduce the concept of resolvable ambiguity wherein a user can resolve an ambiguous program by editing it, as well as an approach to computing the resolutions of an ambiguous program. Furthermore, we present a method based on property-based testing to identify if a composed grammar is unambiguous, resolvably ambiguous, or unresolvably ambiguous. The method is implemented in Haskell and evaluated on a large set of language fragments selected from different languages. The evaluation shows that (i) the approach can handle significantly more cases of language compositions compared to approaches which ban ambiguity altogether, and (ii) that the approach is fast enough to be used in practice.
@InProceedings{CC21p153,
author = {Viktor Palmkvist and Elias Castegren and Philipp Haller and David Broman},
title = {Resolvable Ambiguity: Principled Resolution of Syntactically Ambiguous Programs},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {153--164},
doi = {10.1145/3446804.3446846},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced
CC '21: "Lightning BOLT: Powerful, ..."
Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization
Maksim Panchenko, Rafael Auler, Laith Sakka, and Guilherme Ottoni
(Facebook, USA)
Profile-guided binary optimization has proved to be an important technology to achieve peak performance, particularly for large-scale binaries that are typical for data-center applications. By applying the profile data at the same representation where sampling-based profiling is collected, binary optimizers can provide double-digit speedups over binaries compiled with profile-guided optimizations using similarly collected profile data. The main blocker for adoption of binary optimizers in practice is the overhead that they add to the already long and demanding build pipelines used for producing highly optimized binaries, which already include aggressive compiler optimizations guided by profile data and also link-time optimizations. This paper addresses the overheads of binary optimizers in the context of BOLT, a modern and powerful open-source binary optimizer. More specifically, this paper describes Lightning BOLT, which is an improved version of the BOLT binary optimizer that drastically reduces BOLT’s processing time and memory requirements, while preserving BOLT’s effectiveness in improving the final binary’s performance. Using a set of real-world data-center and open-source applications, we show that Lightning BOLT speeds up BOLT’s processing by an average of 4.71× and reduces BOLT’s memory consumption by 70.5% on average. Furthermore, Lightning BOLT also provides an adjustable mechanism to further reduce BOLT’s overheads at the cost of some lost performance for the final binary.
@InProceedings{CC21p119,
author = {Maksim Panchenko and Rafael Auler and Laith Sakka and Guilherme Ottoni},
title = {Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {119--130},
doi = {10.1145/3446804.3446843},
year = {2021},
}
Publisher's Version
Artifacts Functional
Results Reproduced
CC '21: "Exploring the Space of Optimization ..."
Exploring the Space of Optimization Sequences for Code-Size Reduction: Insights and Tools
Anderson Faustino da Silva, Bernardo N. B. de Lima, and Fernando Magno Quintão Pereira
(State University of Maringá, Brazil; Federal University of Minas Gerais, Brazil)
The optimization space of a compiler is the set of every possible sequence of optimizations that said compiler can use. The exploration of the optimization space of any mainstream compiler has been, for decades, hampered by the lack of benchmarks. However, recent efforts from different research groups have made available a large quantity of compilable code that can be, today, used to overcome this problem. In this paper, we use 15,000 programs from a public collection to explore the optimization space of LLVM, focusing on code-size reduction. This exploration reveals that the probability of beating the default optimization levels of LLVM with random sequences ranges from 10% (considering opt -Oz) to 19% (considering clang -Os). Yet, the distribution of probabilities is uneven across programs: the default levels work well for most programs, and poorly for a few. Based on these observations, we introduce the notion of an Optimization Cache, a table of programs to optimization sequences that can be used to support predictive compilation. We then use an optimization cache to build what we call a Default Covering Set: a small ensemble of optimization sequences that, once combined, tend to be good for any program. Optimization caches and default covering sets are used independently. The former, when applied onto MiBench, yield programs that are 11.9% smaller than programs produced by opt -Os, on average. The latter produce programs 12.5% smaller.
@InProceedings{CC21p47,
author = {Anderson Faustino da Silva and Bernardo N. B. de Lima and Fernando Magno Quintão Pereira},
title = {Exploring the Space of Optimization Sequences for Code-Size Reduction: Insights and Tools},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {47--58},
doi = {10.1145/3446804.3446849},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced
CC '21: "Data-Aware Process Networks ..."
Data-Aware Process Networks
Christophe Alias and Alexandru Plesco
(CNRS, France; ENS Lyon, France; Inria, France; University of Lyon, France; XtremLogic, France)
With the emergence of reconfigurable FPGA circuits as a credible alternative to GPUs for HPC acceleration, new compilation paradigms are required to map high-level algorithmic descriptions to a circuit configuration (High-Level Synthesis, HLS). In particular, novel parallelization algorithms and intermediate representations are required. In this paper, we present the data-aware process networks (DPN), a dataflow intermediate representation suitable for HLS in the context of high-performance computing. DPN combines the benefits of a low-level dataflow representation -- close to the final circuit -- and affine iteration space tiling to explore the parallelization trade-offs (local memory size, communication volume, parallelization degree). We outline our compilation algorithms to map a C program to a DPN (front-end), then to map a DPN to an FPGA configuration (back-end). Finally, we present synthesis results on compute-intensive kernels from the Polybench suite.
@InProceedings{CC21p1,
author = {Christophe Alias and Alexandru Plesco},
title = {Data-Aware Process Networks},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {1--11},
doi = {10.1145/3446804.3446847},
year = {2021},
}
Publisher's Version
CC '21: "PolyBench/Python: Benchmarking ..."
PolyBench/Python: Benchmarking Python Environments with Polyhedral Optimizations
Miguel Á. Abella-González, Pedro Carollo-Fernández, Louis-Noël Pouchet, Fabrice Rastello, and Gabriel Rodríguez
(Universidade da Coruña, Spain; Colorado State University, USA; Inria, France)
Python has become one of the most used and taught languages nowadays. Its expressiveness, cross-compatibility and ease of use have made it popular in areas as diverse as finance, bioinformatics or machine learning. However, Python programs are often significantly slower to execute than an equivalent native C implementation, especially for computation-intensive numerical kernels.
This work presents PolyBench/Python, implementing the 30 kernels in PolyBench/C, one of the standard benchmark suites for polyhedral optimization, in Python. In addition to the benchmark kernels, a functional wrapper including mechanisms for performance measurement, testing, and execution configuration has been developed. The framework includes support for different ways to translate C-array codes into Python, offering insight into the tradeoffs of Python lists and NumPy arrays. The benchmark performance is thoroughly evaluated on different Python interpreters, and compared against its PolyBench/C counterpart to highlight the profitability (or lack thereof) of using Python for regular numerical codes.
@InProceedings{CC21p59,
author = {Miguel Á. Abella-González and Pedro Carollo-Fernández and Louis-Noël Pouchet and Fabrice Rastello and Gabriel Rodríguez},
title = {PolyBench/Python: Benchmarking Python Environments with Polyhedral Optimizations},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {59--70},
doi = {10.1145/3446804.3446842},
year = {2021},
}
Publisher's Version
Artifacts Reusable
CC '21: "A Modern Compiler for the ..."
A Modern Compiler for the French Tax Code
Denis Merigoux, Raphaël Monat, and Jonathan Protzenko
(Inria, France; Sorbonne University, France; CNRS, France; LIP6, France; Microsoft Research, USA)
In France, income tax is computed from taxpayers' individual returns, using an algorithm that is authored, designed and maintained by the French Public Finances Directorate (DGFiP). This algorithm relies on a legacy custom language and compiler originally designed in 1990, which unlike French wine, did not age well with time. Owing to the shortcomings of the input language and the technical limitations of the compiler, the algorithm is proving harder and harder to maintain, relying on ad-hoc behaviors and workarounds to implement the most recent changes in tax law. Competence loss and aging code also mean that the system does not benefit from any modern compiler techniques that would increase confidence in the implementation.
We overhaul this infrastructure and present Mlang, an open-source compiler toolchain whose goal is to replace the existing infrastructure. Mlang is based on a reverse-engineered formalization of the DGFiP's system, and has been thoroughly validated against the private DGFiP test suite. As such, Mlang has a formal semantics; eliminates previous hand-written workarounds in C; compiles to modern languages (Python); and enables a variety of instrumentations, providing deep insights about the essence of French income tax computation. The DGFiP is now officially transitioning to Mlang for their production system.
@InProceedings{CC21p71,
author = {Denis Merigoux and Raphaël Monat and Jonathan Protzenko},
title = {A Modern Compiler for the French Tax Code},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {71--82},
doi = {10.1145/3446804.3446850},
year = {2021},
}
Publisher's Version
Info
Artifacts Reusable
Results Reproduced
CC '21: "PGZ: Automatic Zero-Value ..."
PGZ: Automatic Zero-Value Code Specialization
Mark Stephenson and Ram Rangan
(NVIDIA, USA; NVIDIA, India)
In prior work we proposed Zeroploit, a transform that duplicates code, specializes one path assuming certain key program operands, called versioning variables, are zero, and leaves the other path unspecialized. Dynamically, depending on the versioning variable’s value, either the specialized fast path or the default slow path will execute. We evaluated Zeroploit with hand-optimized codes in that work.
In this paper, we present PGZ, a completely automated, profile-guided compiler approach for Zeroploit. Our compiler automatically determines which versioning variables or combinations thereof are profitable, and determines the code region to duplicate and specialize. PGZ’s heuristic takes operand zero value probabilities as input and it then uses classical techniques such as constant folding and dead-code elimination to estimate the potential savings of specializing a versioning variable. PGZ transforms profitable candidates, yielding an average speedup of 21.2% for targeted shader programs, and an average frame-rate speedup of 4.4% across a collection of modern gaming applications on an NVIDIA GeForce RTX 2080 GPU.
@InProceedings{CC21p36,
author = {Mark Stephenson and Ram Rangan},
title = {PGZ: Automatic Zero-Value Code Specialization},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {36--46},
doi = {10.1145/3446804.3446845},
year = {2021},
}
Publisher's Version
CC '21: "PolyBench/Python: Benchmarking ..."
PolyBench/Python: Benchmarking Python Environments with Polyhedral Optimizations
Miguel Á. Abella-González, Pedro Carollo-Fernández, Louis-Noël Pouchet, Fabrice Rastello, and Gabriel Rodríguez
(Universidade da Coruña, Spain; Colorado State University, USA; Inria, France)
Python has become one of the most used and taught languages nowadays. Its expressiveness, cross-compatibility and ease of use have made it popular in areas as diverse as finance, bioinformatics or machine learning. However, Python programs are often significantly slower to execute than an equivalent native C implementation, especially for computation-intensive numerical kernels.
This work presents PolyBench/Python, implementing the 30 kernels in PolyBench/C, one of the standard benchmark suites for polyhedral optimization, in Python. In addition to the benchmark kernels, a functional wrapper including mechanisms for performance measurement, testing, and execution configuration has been developed. The framework includes support for different ways to translate C-array codes into Python, offering insight into the tradeoffs of Python lists and NumPy arrays. The benchmark performance is thoroughly evaluated on different Python interpreters, and compared against its PolyBench/C counterpart to highlight the profitability (or lack thereof) of using Python for regular numerical codes.
@InProceedings{CC21p59,
author = {Miguel Á. Abella-González and Pedro Carollo-Fernández and Louis-Noël Pouchet and Fabrice Rastello and Gabriel Rodríguez},
title = {PolyBench/Python: Benchmarking Python Environments with Polyhedral Optimizations},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {59--70},
doi = {10.1145/3446804.3446842},
year = {2021},
}
Publisher's Version
Artifacts Reusable
CC '21: "PolyBench/Python: Benchmarking ..."
PolyBench/Python: Benchmarking Python Environments with Polyhedral Optimizations
Miguel Á. Abella-González, Pedro Carollo-Fernández, Louis-Noël Pouchet, Fabrice Rastello, and Gabriel Rodríguez
(Universidade da Coruña, Spain; Colorado State University, USA; Inria, France)
Python has become one of the most used and taught languages nowadays. Its expressiveness, cross-compatibility and ease of use have made it popular in areas as diverse as finance, bioinformatics or machine learning. However, Python programs are often significantly slower to execute than an equivalent native C implementation, especially for computation-intensive numerical kernels.
This work presents PolyBench/Python, implementing the 30 kernels in PolyBench/C, one of the standard benchmark suites for polyhedral optimization, in Python. In addition to the benchmark kernels, a functional wrapper including mechanisms for performance measurement, testing, and execution configuration has been developed. The framework includes support for different ways to translate C-array codes into Python, offering insight into the tradeoffs of Python lists and NumPy arrays. The benchmark performance is thoroughly evaluated on different Python interpreters, and compared against its PolyBench/C counterpart to highlight the profitability (or lack thereof) of using Python for regular numerical codes.
@InProceedings{CC21p59,
author = {Miguel Á. Abella-González and Pedro Carollo-Fernández and Louis-Noël Pouchet and Fabrice Rastello and Gabriel Rodríguez},
title = {PolyBench/Python: Benchmarking Python Environments with Polyhedral Optimizations},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {59--70},
doi = {10.1145/3446804.3446842},
year = {2021},
}
Publisher's Version
Artifacts Reusable
CC '21: "Lightning BOLT: Powerful, ..."
Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization
Maksim Panchenko, Rafael Auler, Laith Sakka, and Guilherme Ottoni
(Facebook, USA)
Profile-guided binary optimization has proved to be an important technology to achieve peak performance, particularly for large-scale binaries that are typical for data-center applications. By applying the profile data at the same representation where sampling-based profiling is collected, binary optimizers can provide double-digit speedups over binaries compiled with profile-guided optimizations using similarly collected profile data. The main blocker for adoption of binary optimizers in practice is the overhead that they add to the already long and demanding build pipelines used for producing highly optimized binaries, which already include aggressive compiler optimizations guided by profile data and also link-time optimizations. This paper addresses the overheads of binary optimizers in the context of BOLT, a modern and powerful open-source binary optimizer. More specifically, this paper describes Lightning BOLT, which is an improved version of the BOLT binary optimizer that drastically reduces BOLT’s processing time and memory requirements, while preserving BOLT’s effectiveness in improving the final binary’s performance. Using a set of real-world data-center and open-source applications, we show that Lightning BOLT speeds up BOLT’s processing by an average of 4.71× and reduces BOLT’s memory consumption by 70.5% on average. Furthermore, Lightning BOLT also provides an adjustable mechanism to further reduce BOLT’s overheads at the cost of some lost performance for the final binary.
@InProceedings{CC21p119,
author = {Maksim Panchenko and Rafael Auler and Laith Sakka and Guilherme Ottoni},
title = {Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {119--130},
doi = {10.1145/3446804.3446843},
year = {2021},
}
Publisher's Version
Artifacts Functional
Results Reproduced
CC '21: "Deep NLP-Based Co-evolvement ..."
Deep NLP-Based Co-evolvement for Synthesizing Code Analysis from Natural Language
Zifan Nan, Hui Guan, Xipeng Shen, and Chunhua Liao
(North Carolina State University, USA; University of Massachusetts at Amherst, USA; Lawrence Livermore National Laboratory, USA)
This paper presents Deepsy, a Natural Language-based synthesizer to assist source code analysis. It takes English descriptions of to-be-found code patterns as its inputs, and automatically produces ASTMatcher expressions that are directly usable by LLVM/Clang to materialize intended code analysis. The code analysis domain features profuse complexities in data types and operations, which make it elusive for prior rule-based synthesizers to tackle. On the other hand, machine learning-based solutions are neither applicable due to the scarcity of well labeled examples. This paper presents how Deepsy addresses the challenges by leveraging deep Natural Language Processing (NLP) and creating a new technique named dependency tree-based co-evolvement.Deepsy features an effective design that seamlessly integrates Natural Language dependency analysis into code analysis and meanwhile synergizes it with type-based narrowing and domain-specific guidance. Deepsy achieves over 70.0% expression-level accuracy and 85.1% individual API-level accuracy, significantly outperforming previous solutions.
@InProceedings{CC21p141,
author = {Zifan Nan and Hui Guan and Xipeng Shen and Chunhua Liao},
title = {Deep NLP-Based Co-evolvement for Synthesizing Code Analysis from Natural Language},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {141--152},
doi = {10.1145/3446804.3446852},
year = {2021},
}
Publisher's Version
CC '21: "Exploring the Space of Optimization ..."
Exploring the Space of Optimization Sequences for Code-Size Reduction: Insights and Tools
Anderson Faustino da Silva, Bernardo N. B. de Lima, and Fernando Magno Quintão Pereira
(State University of Maringá, Brazil; Federal University of Minas Gerais, Brazil)
The optimization space of a compiler is the set of every possible sequence of optimizations that said compiler can use. The exploration of the optimization space of any mainstream compiler has been, for decades, hampered by the lack of benchmarks. However, recent efforts from different research groups have made available a large quantity of compilable code that can be, today, used to overcome this problem. In this paper, we use 15,000 programs from a public collection to explore the optimization space of LLVM, focusing on code-size reduction. This exploration reveals that the probability of beating the default optimization levels of LLVM with random sequences ranges from 10% (considering opt -Oz) to 19% (considering clang -Os). Yet, the distribution of probabilities is uneven across programs: the default levels work well for most programs, and poorly for a few. Based on these observations, we introduce the notion of an Optimization Cache, a table of programs to optimization sequences that can be used to support predictive compilation. We then use an optimization cache to build what we call a Default Covering Set: a small ensemble of optimization sequences that, once combined, tend to be good for any program. Optimization caches and default covering sets are used independently. The former, when applied onto MiBench, yield programs that are 11.9% smaller than programs produced by opt -Os, on average. The latter produce programs 12.5% smaller.
@InProceedings{CC21p47,
author = {Anderson Faustino da Silva and Bernardo N. B. de Lima and Fernando Magno Quintão Pereira},
title = {Exploring the Space of Optimization Sequences for Code-Size Reduction: Insights and Tools},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {47--58},
doi = {10.1145/3446804.3446849},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced
CC '21: "Integrating a Functional Pattern-Based ..."
Integrating a Functional Pattern-Based IR into MLIR
Martin Lücke, Michel Steuwer, and Aaron Smith
(University of Edinburgh, UK; Microsoft, USA)
The continued specialization in hardware and software due to the end of Moore's law forces us to question fundamental design choices in compilers, and in particular for domain specific languages. The days where a single universal compiler intermediate representation (IR) was sufficient to perform all important optimizations are over. We need novel IRs and ways for them to interact with one another while leveraging established compiler infrastructures.
In this paper, we present a practical implementation of a functional pattern-based IR in the SSA-based MLIR framework. Our IR captures the program semantics as compositions of common computational patterns enabling rewrite-based optimizations. We discuss the integration with other IRs by demonstrating the compilation of a neural network represented as a TensorFlow graph down to optimized LLVM code via our functional pattern-based IR. Our implementation demonstrates for the first time a practical integration of a functional pattern-based IR with other IRs and it enables the construction of sophisticated code generators for domain specific languages.
@InProceedings{CC21p12,
author = {Martin Lücke and Michel Steuwer and Aaron Smith},
title = {Integrating a Functional Pattern-Based IR into MLIR},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {12--22},
doi = {10.1145/3446804.3446844},
year = {2021},
}
Publisher's Version
Artifacts Functional
Results Reproduced
CC '21: "PGZ: Automatic Zero-Value ..."
PGZ: Automatic Zero-Value Code Specialization
Mark Stephenson and Ram Rangan
(NVIDIA, USA; NVIDIA, India)
In prior work we proposed Zeroploit, a transform that duplicates code, specializes one path assuming certain key program operands, called versioning variables, are zero, and leaves the other path unspecialized. Dynamically, depending on the versioning variable’s value, either the specialized fast path or the default slow path will execute. We evaluated Zeroploit with hand-optimized codes in that work.
In this paper, we present PGZ, a completely automated, profile-guided compiler approach for Zeroploit. Our compiler automatically determines which versioning variables or combinations thereof are profitable, and determines the code region to duplicate and specialize. PGZ’s heuristic takes operand zero value probabilities as input and it then uses classical techniques such as constant folding and dead-code elimination to estimate the potential savings of specializing a versioning variable. PGZ transforms profitable candidates, yielding an average speedup of 21.2% for targeted shader programs, and an average frame-rate speedup of 4.4% across a collection of modern gaming applications on an NVIDIA GeForce RTX 2080 GPU.
@InProceedings{CC21p36,
author = {Mark Stephenson and Ram Rangan},
title = {PGZ: Automatic Zero-Value Code Specialization},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {36--46},
doi = {10.1145/3446804.3446845},
year = {2021},
}
Publisher's Version
CC '21: "Integrating a Functional Pattern-Based ..."
Integrating a Functional Pattern-Based IR into MLIR
Martin Lücke, Michel Steuwer, and Aaron Smith
(University of Edinburgh, UK; Microsoft, USA)
The continued specialization in hardware and software due to the end of Moore's law forces us to question fundamental design choices in compilers, and in particular for domain specific languages. The days where a single universal compiler intermediate representation (IR) was sufficient to perform all important optimizations are over. We need novel IRs and ways for them to interact with one another while leveraging established compiler infrastructures.
In this paper, we present a practical implementation of a functional pattern-based IR in the SSA-based MLIR framework. Our IR captures the program semantics as compositions of common computational patterns enabling rewrite-based optimizations. We discuss the integration with other IRs by demonstrating the compilation of a neural network represented as a TensorFlow graph down to optimized LLVM code via our functional pattern-based IR. Our implementation demonstrates for the first time a practical integration of a functional pattern-based IR with other IRs and it enables the construction of sophisticated code generators for domain specific languages.
@InProceedings{CC21p12,
author = {Martin Lücke and Michel Steuwer and Aaron Smith},
title = {Integrating a Functional Pattern-Based IR into MLIR},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {12--22},
doi = {10.1145/3446804.3446844},
year = {2021},
}
Publisher's Version
Artifacts Functional
Results Reproduced
CC '21: "Helper Function Inlining in ..."
Helper Function Inlining in Dynamic Binary Translation
Wenwen Wang
(University of Georgia, USA)
Dynamic binary translation (DBT) is the cornerstone of many important applications. Yet, it takes a tremendous effort to develop and maintain a real-world DBT system. To mitigate the engineering effort, helper functions are frequently employed during the development of a DBT system. Though helper functions greatly facilitate the DBT development, their adoption incurs substantial performance overhead due to the helper function calls. To solve this problem, this paper presents a novel approach to inline helper functions in DBT systems. The proposed inlining approach addresses several unique technical challenges. As a result, the performance overhead introduced by helper function calls can be reduced, and meanwhile, the benefits of helper functions for DBT development are not lost. We have implemented a prototype based on the proposed inlining approach using a popular DBT system, QEMU. Experimental results on the benchmark programs from the SPEC CPU 2017 benchmark suite show that an average of 1.2x performance speedup can be achieved. Moreover, the translation overhead introduced by inlining helper functions is negligible.
@InProceedings{CC21p107,
author = {Wenwen Wang},
title = {Helper Function Inlining in Dynamic Binary Translation},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {107--118},
doi = {10.1145/3446804.3446851},
year = {2021},
}
Publisher's Version
CC '21: "Communication-Safe Web Programming ..."
Communication-Safe Web Programming in TypeScript with Routed Multiparty Session Types
Anson Miu, Francisco Ferreira, Nobuko Yoshida, and Fangyi Zhou
(Imperial College London, UK; Bloomberg, UK)
Modern web programming involves coordinating interactions between browser clients and a server. Typically, the interactions in web-based distributed systems are informally described, making it hard to ensure correctness, especially communication safety, i.e. all endpoints progress without type errors or deadlocks, conforming to a specified protocol.
We present STScript, a toolchain that generates TypeScript APIs for communication-safe web development over WebSockets, and RouST, a new session type theory that supports multiparty communications with routing mechanisms. STScript provides developers with TypeScript APIs generated from a communication protocol specification based on RouST. The generated APIs build upon TypeScript concurrency practices, complement the event-driven style of programming in full-stack web development, and are compatible with the Node.js runtime for server-side endpoints and the React.js framework for browser-side endpoints.
RouST can express multiparty interactions routed via an intermediate participant. It supports peer-to-peer communication between browser-side endpoints by routing communication via the server in a way that avoids excessive serialisation. RouST guarantees communication safety for endpoint web applications written using STScript APIs.
We evaluate the expressiveness of STScript for modern web programming using several production-ready case studies deployed as web applications.
@InProceedings{CC21p94,
author = {Anson Miu and Francisco Ferreira and Nobuko Yoshida and Fangyi Zhou},
title = {Communication-Safe Web Programming in TypeScript with Routed Multiparty Session Types},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {94--106},
doi = {10.1145/3446804.3446854},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced
CC '21: "Communication-Safe Web Programming ..."
Communication-Safe Web Programming in TypeScript with Routed Multiparty Session Types
Anson Miu, Francisco Ferreira, Nobuko Yoshida, and Fangyi Zhou
(Imperial College London, UK; Bloomberg, UK)
Modern web programming involves coordinating interactions between browser clients and a server. Typically, the interactions in web-based distributed systems are informally described, making it hard to ensure correctness, especially communication safety, i.e. all endpoints progress without type errors or deadlocks, conforming to a specified protocol.
We present STScript, a toolchain that generates TypeScript APIs for communication-safe web development over WebSockets, and RouST, a new session type theory that supports multiparty communications with routing mechanisms. STScript provides developers with TypeScript APIs generated from a communication protocol specification based on RouST. The generated APIs build upon TypeScript concurrency practices, complement the event-driven style of programming in full-stack web development, and are compatible with the Node.js runtime for server-side endpoints and the React.js framework for browser-side endpoints.
RouST can express multiparty interactions routed via an intermediate participant. It supports peer-to-peer communication between browser-side endpoints by routing communication via the server in a way that avoids excessive serialisation. RouST guarantees communication safety for endpoint web applications written using STScript APIs.
We evaluate the expressiveness of STScript for modern web programming using several production-ready case studies deployed as web applications.
@InProceedings{CC21p94,
author = {Anson Miu and Francisco Ferreira and Nobuko Yoshida and Fangyi Zhou},
title = {Communication-Safe Web Programming in TypeScript with Routed Multiparty Session Types},
booktitle = {Proc.\ CC},
publisher = {ACM},
pages = {94--106},
doi = {10.1145/3446804.3446854},
year = {2021},
}
Publisher's Version
Artifacts Reusable
Results Reproduced