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code Synthesis for Sparse Tensor Format Conversion and optimization 2023

Code Synthesis for Sparse Tensor Format Conversion and Optim...

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21st acm/ieee international symposium on code generation and optimization (CGO)

作者： Popoola, Tobi Zhao, Tuowen St George, Aaron Bhetwal, Kalyan Strout, Michelle Mills Hall, Mary Olschanowsky, Catherine Boise State Univ Comp Sci Boise ID 83725 USA Univ Utah Comp Sci Salt Lake City UT 84112 USA Univ Arizona Comp Sci Tucson AZ 85721 USA Univ Utah Sch Comp Salt Lake City UT 84112 USA

ISBN: (纸本)9798400701016

Many scientific applications compute using sparse data and store that data in a variety of sparse formats because each format has unique space and performance benefits. Optimizing applications that use sparse data involves translating the sparse data into the chosen format and transforming the computation to iterate over that format. This paper presents a formal definition of sparse tensor formats and an automated approach to synthesize the transformation between formats. This approach is unique in that it supports ordering constraints not supported by other approaches and synthesizes the transformation code in a high-level intermediate representation suitable for applying composable transformations such as loop fusion and temporary storage reduction. We demonstrate that the synthesized code for COO to CSR with optimizations is 2.85x faster than TACO, Intel MKL, and SPARSKIT while the more complex COO to DIA is 1.4x slower than TACO but faster than SPARSKIT and Intel MKL using the geometric average of execution time.

关键词： Sparse Format Conversion Sparser Format Descriptors Polyhedral compilation code synthesis Uninterpreted functions Index array properties Transformations

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Khaos: The Impact of Inter-procedural code Obfuscation on Binary Diffing Techniques 2023

Khaos: The Impact of Inter-procedural Code Obfuscation on Bi...

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21st acm/ieee international symposium on code generation and optimization (CGO)

作者： Zhang, Peihua Wu, Chenggang Peng, Mingfan Zeng, Kai Yu, Ding Lai, Yuanming Kang, Yan Wang, Wei Wang, Zhe Chinese Acad Sci Inst Comp Technol SKLP Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China

ISBN: (纸本)9798400701016

Software obfuscation techniques can prevent binary diffing techniques from locating vulnerable code by obfuscating the third-party code, to achieve the purpose of protecting embedded device software. With the rapid development of binary diffing techniques, they can achieve more and more accurate function matching and identification by extracting the features within the function. This makes existing software obfuscation techniques, which mainly focus on the intra-procedural code obfuscation, no longer effective. In this paper, we propose a new inter-procedural code obfuscation mechanism Khaos, which moves the code across functions to obfuscate the function by using compilation optimizations. Two obfuscation primitives are proposed to separate and aggregate the function, which are called fission and fusion respectively. A prototype of Khaos is implemented based on the LLVM compiler and evaluated on a large number of real-world programs including SPEC CPU 2006 & 2017, CoreUtils, JavaScript engines, etc. Experimental results show that Khaos outperforms existing code obfuscations and can significantly reduce the accuracy rates of five state-of-the-art binary diffing techniques (less than 19%) with lower runtime overhead (less than 7%).

关键词： Software Protection Obfuscation Binary Diffing

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Vacuum packing: Extracting hardware-detected program phases for post-link optimization 35

Vacuum packing: Extracting hardware-detected program phases ...

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35th annual ieee/acm international symposium on Microarchitecture

作者： Barnes, RD Nystrom, EM Mertern, MC Hwu, WMW Univ Illinois Ctr Reliable & High Performance Comp Dept Elect & Comp Engn Urbana IL 61801 USA

ISBN: (纸本)0769518591

This paper presents Vacuum Packing, a new approach to profile-based program optimization. Instead of using traditional aggregate or summarized execution profile weights, this approach uses a transparent hardware profiler to automatically detect execution phases and record branch profile information for each new phase. The code extraction algorithm then produces code packages that are specially formed for their corresponding phases. The algorithm compensates for the incomplete and often incoherent branch profile information that arises due to the nature of hardware profilers. The technique avoids unnecessary code replication by focusing on hot code, making efficient connections between the original code and the new code, linking code packages at select points to facilitate phase transitions, and providing a platform for efficient optimization. We demonstrate that using a concise set of profile information from a hardware profiler we can generate code packages, specialized for each phase of execution, that capture more than 80% of the average total program execution. We further show that the approach is very effective in extracting code regions that capture the phasing behavior of programs, that the code size increase is moderate, and that the code regions benefit from sample optimizations.

关键词： Aggregates Computer architecture Data mining Emulation Java Joining processes Libraries Packaging Phase detection Reliability engineering

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Automatic generation of High-Performance Quantized Machine Learning Kernels 2020

Automatic Generation of High-Performance Quantized Machine L...

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18th acm/ieee international symposium on code generation and optimization (CGO)

作者： Cowan, Meghan Moreau, Thierry Chen, Tianqi Bornholt, James Ceze, Luis Univ Washington Seattle WA 98195 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781450370479

Quantization optimizes machine learning inference for resource constrained environments by reducing the precision of its computation. In the extreme, even single-bit computations can produce acceptable results at dramatically lower cost. But this ultra-low-precision quantization is difficult to exploit because extracting optimal performance requires hand-tuning both high-level scheduling decisions and low-level implementations. As a result, practitioners settle for a few predefined quantized kernels, sacrificing optimality and restricting their ability to adapt to new hardware. This paper presents a new automated approach to implementing quantized inference for machine learning models. We integrate the choice of how to lay out quantized values into the scheduling phase of a machine learning compiler, allowing it to be optimized in concert with tiling and parallelization decisions. After scheduling, we use program synthesis to automatically generate efficient low-level operator implementations for the desired precision and data layout. We scale up synthesis using a novel reduction sketch that exploits the structure of matrix multiplication. On a ResNet18 model, our generated code outperforms an optimized floating-point baseline by up to 3. 9x, and a state-of the-art quantized implementation by up to 16.6x.

关键词： quantization machine learning synthesis

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ANGHABENCH: A Suite with One Million Compilable C Benchmarks for code-Size Reduction 21

ANGHABENCH: A Suite with One Million Compilable C Benchmarks...

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19th ieee/acm international symposium on code generation and optimization (CGO)

作者： da Silva, Anderson Faustino Kind, Bruno Conde de Souza Magalhaes, Jose Wesley Rocha, Jeronimo Nunes Ferreira Guimaraes, Breno Campos Quinao Pereira, Fernando Magno Univ Estadual Maringa Dept Informat Maringa Parana Brazil Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9781728186139

A predictive compiler uses properties of a program to decide how to optimize it. The compiler is trained on a collection of programs to derive a model which determines its actions in face of unknown codes. One of the challenges of predictive compilation is how to find good training sets. Regardless of the programming language, the availability of human-made benchmarks is limited. Moreover, current synthesizers produce code that is very different from actual programs, and mining compilable code from open repositories is difficult, due to program dependencies. In this paper, we use a combination of web crawling and type inference to overcome these problems for the C programming language. We use a type reconstructor based on Hindley-Milner's algorithm to produce ANGHABENCH, a virtually unlimited collection of real-world compilable C programs. Although ANGHABENCH programs are not executable, they can be transformed into object files by any C compliant compiler. Therefore, they can be used to train compilers for code size reduction. We have used thousands of ANGHABENCH programs to train YACOS, a predictive compiler based on LLVM. The version of YACOS autotuned with ANGHABENCH generates binaries for the LLVM test suite over 10% smaller than clang -Oz. It compresses code impervious even to the state-of-the-art Function Sequence Alignment technique published in 2019, as it does not require large binaries to work well.

关键词： Benchmark Repository Synthesis Training

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JSWhiz Static Analysis for JavaScript Memory Leaks

JSWhiz Static Analysis for JavaScript Memory Leaks

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11th ieee/acm international symposium on code generation and optimization (CGO)

作者： Pienaar, Jacques A. Hundt, Robert Purdue Univ W Lafayette IN 47907 USA Google Inc Mountain View CA USA

ISBN: (纸本)9781467355254;9781467355247

JavaScript is the dominant language for implementing dynamic web pages in browsers. Even though it is standardized, many browsers implement language and browser bindings in different and incompatible ways. As a result, a plethora of web development frameworks were developed to hide cross-browser issues and to ease development of large web applications. An unwelcome side-effect of these frameworks is that they can introduce memory leaks, despite the fact that JavaScript is garbage collected. Memory bloat is a major issue for web applications, as it affects user perceived latency and may even prevent large web applications from running on devices with limited resources. In this paper we present JSWhiz, an extension to the open-source Closure JavaScript compiler. Based on experiences analyzing memory leaks in Gmail, JSWhiz detects five identified common problem patterns. JSWhiz found a total of 89 memory leaks across Google's Gmail, Docs, Spreadsheets, Books, and Closure itself. It contributed significantly in a recent effort to reduce Gmail memory footprint, which resulted in bloat reduction of 75% at the 99th percentile, and by roughly 50% at the median.

关键词： optimization Performance Static Analysis Memory Leak JavaScript

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Language and Compiler Support for Auto-Tuning Variable-Accuracy Algorithms

Language and Compiler Support for Auto-Tuning Variable-Accur...

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9th annual ieee/acm international symposium on code generation and optimization (CGO)

作者： Ansel, Jason Wong, Yee Lok Chan, Cy Olszewski, Marek Edelman, Alan Amarasinghe, Saman MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

ISBN: (纸本)9781612843551

Approximating ideal program outputs is a common technique for solving computationally difficult problems, for adhering to processing or timing constraints, and for performance optimization in situations where perfect precision is not necessary. To this end, programmers often use approximation algorithms, iterative methods, data resampling, and other heuristics. However, programming such variable accuracy algorithms presents difficult challenges since the optimal algorithms and parameters may change with different accuracy requirements and usage environments. This problem is further compounded when multiple variable accuracy algorithms are nested together due to the complex way that accuracy requirements can propagate across algorithms and because of the size of the set of allowable compositions. As a result, programmers often deal with this issue in an ad-hoc manner that can sometimes violate sound programming practices such as maintaining library abstractions. In this paper, we propose language extensions that expose trade-offs between time and accuracy to the compiler. The compiler performs fully automatic compile-time and install-time autotuning and analyses in order to construct optimized algorithms to achieve any given target accuracy. We present novel compiler techniques and a structured genetic tuning algorithm to search the space of candidate algorithms and accuracies in the presence of recursion and sub-calls to other variable accuracy code. These techniques benefit both the library writer, by providing an easy way to describe and search the parameter and algorithmic choice space, and the library user, by allowing high level specification of accuracy requirements which are then met automatically without the need for the user to understand any algorithm-specific parameters. Additionally, we present a new suite of benchmarks, written in our language, to examine the efficacy of our techniques. Our experimental results show that by relaxing accuracy requirement

关键词： required precision Language compilers Self tuning algorithms optimization algorithms Recursion

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Program State Element Characterization 2023

Program State Element Characterization

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21st acm/ieee international symposium on code generation and optimization (CGO)

作者： Deiana, Enrico Armenio Suchy, Brian Wilkins, Michael Homerding, Brian McMichen, Tommy Dunajewski, Katarzyna Dinda, Peter Hardavellas, Nikos Campanoni, Simone Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9798400701016

Modern programming languages offer abstractions that simplify software development and allow hardware to reach its full potential. These abstractions range from the well-established OpenMP language extensions to newer C++ features like smart pointers. To properly use these abstractions in an existing codebase, programmers must determine how a given source code region interacts with Program State Elements (PSEs) (i.e., the program's variables and memory locations). We call this process Program State Element Characterization (PSEC). Without tool support for PSEC, a programmer's only option is to manually study the entire codebase. We propose a profile-based approach that automates PSEC and provides abstraction recommendations to programmers. Because a profile-based approach incurs an impractical overhead, we introduce the Compiler and Runtime Memory Observation Tool (CARMOT), a PSEC-specific compiler co-designed with a parallel runtime. CARMOT reduces the overhead of PSEC by two orders of magnitude, making PSEC practical. We show that CARMOT's recommendations achieve the same speedup as hand-tuned OpenMP directives and avoid memory leaks with C++ smart pointers. From this, we argue that PSEC tools, such as CARMOT, can provide support for the rich ecosystem of modern programming language abstractions.

关键词： code optimization dynamic analysis program characterization

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DJXPerf: Identifying Memory Inefficiencies via Object-Centric Profiling for Java 2023

DJXPerf: Identifying Memory Inefficiencies via Object-Centri...

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21st acm/ieee international symposium on code generation and optimization (CGO)

作者： Li, Bolun Su, Pengfei Chabbi, Milind Jiao, Shuyin Liu, Xu North Carolina State Univ Raleigh NC 27695 USA Univ Calif Merced Merced CA USA Scalable Machines Res San Jose CA USA

ISBN: (纸本)9798400701016

Java is the "go-to" programming language choice for developing scalable enterprise cloud applications. In such systems, even a few percent CPU time savings can offer a significant competitive advantage and cost savings. Although performance tools abound for Java, those that focus on the data locality in the memory hierarchy are rare. In this paper, we first categorize data locality issues in Java programs. We then present DJXPerf, a lightweight, objectcentric memory profiler for Java, which associates memoryhierarchy performance metrics (e.g., cache/TLB misses) with Java objects. DJXPerf uses statistical sampling of hardware performance monitoring counters to attribute metrics to not only source code locations but also Java objects. DJXPerf presents Java object allocation contexts combined with their usage contexts and presents them ordered by the poor locality behaviors. DJXPerf's performance measurement, object attribution, and presentation techniques guide optimizing object allocation, layout, and access patterns. DJXPerf incurs only similar to 8.5% runtime overhead and similar to 6% memory overhead on average, requiring no modifications to hardware, OS, Java virtual machine, or application source code, which makes it attractive to use in production. Guided by DJXPerf, we study and optimize a number of Java and Scala programs, including well-known benchmarks and real-world applications, and demonstrate significant speedups.

关键词： Java profiling performance optimization PMU

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Parsimony: Enabling SIMD/Vector Programming in Standard Compiler Flows 2023

Parsimony: Enabling SIMD/Vector Programming in Standard Comp...

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21st acm/ieee international symposium on code generation and optimization (CGO)

作者： Kandiah, Vijay Lustig, Daniel Villa, Oreste Nellans, David Hardavellas, Nikos Northwestern Univ Evanston IL 60208 USA NVIDIA San Jose CA USA

ISBN: (纸本)9798400701016

Achieving peak throughput on modern CPUs requires maximizing the use of single-instruction, multiple-data (SIMD) or vector compute units. Single-program, multiple-data (SPMD) programming models are an effective way to use high-level programming languages to target these ISAs. Unfortunately, many SPMD frameworks have evolved to have either overly-restrictive language specifications or under-specified programming models, and this has slowed the widescale adoption of SPMD-style programming. This paper introduces Parsimony (PARallel SIMd), a SPMD programming approach built with semantics designed to be compatible with multiple languages and to cleanly integrate into the standard optimizing compiler toolchains for those languages. We first explain the Parsimony programming model semantics and how they enable a standalone compiler IR-to-IR pass that can perform vectorization independently of other passes, improving the language and toolchain compatibility of SPMD programming. We then demonstrate a LLVM prototype of the Parsimony approach that matches the performance of ispc, a popular but more restrictive SPMD approach, and achieves 97% of the performance of hand-written AVX-512 SIMD intrinsics on over 70 benchmarks ported from the Simd Library. We finally discuss where Parsimony has exposed parts of existing language and compiler flows where slight improvements could further enable improved SPMD program vectorization.

关键词： Parallel Computing Vectorization code Translation Single-instruction Multiple-data Compiler Design

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