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31st Conference on Computer and Communications Security

作者： Zhang, Yue Sirlanci, Melih Wang, Ruoyu Lin, Zhiqiang Drexel Univ Philadelphia PA 19104 USA Ohio State Univ Columbus OH USA Arizona State Univ Tempe AZ USA

ISBN: (纸本)9798400706363

compiler optimizations intend to transform a program into a semantic-equivalent one with improved performance, but it is unclear how these optimizations may impact the performance of dynamic symbolic execution (DSE) on binary code. To systematically understand the impact of compiler optimizations on two popular DSE techniques (i.e., symbolic exploration and symbolic tracing), this paper presents an empirical study that quantifies 209 GCC compilation flags and 73 Clang compilation flags to reveal both positive and negative optimizations to DSE. Our data set contains 992 unique test cases, which are produced from 3,449 source files in the GCC test suite. After analyzing 2,978,976 binary programs that we compiled with two compilers and various compilation flags, we found that although some optimizations make DSE faster, most optimizations will actually slow down DSE. Our analysis further reveals root causes behind these impacts. The most positive impacts that optimizations have on DSE come from the reduction of the number of instructions and program paths, whereas negative impacts are caused by a series of unexpected behaviors, including increased numbers of instructions or program paths, library function inlining preventing DSE engines from using function summaries, and arithmetic optimizations leading to more sophisticated constraints. Being the first in-depth analysis on why compiler flags influence the performance of DSE, this project sheds light on program transformations that can be applied before performing DSE tasks for better performance.

关键词： compiler optimization Symbolic Execution Measurement Study

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Integrated compiler optimizations for tensor contractions

Integrated compiler optimizations for tensor contractions

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作者： Gao, Xiaoyang The Ohio State University

学位级别：Ph.D.

This dissertation addresses several performance optimization issues in the context of the Tensor Contraction Engine (TCE), a domain-specific compiler to synthesize parallel, out-of-core programs for a class of scientific computations encountered in computational chemistry and physics. The domain of our focus is electronic structure calculations, where many computationally intensive components are expressible as a set of tensor contractions. These scientific applications are extremely compute-intensive and consume significant computer resources at national supercomputer centers. The manual development of high-performance parallel programs for them is usually very tedious and time consuming. The TCE system is targeted at reducing the burden on application scientists, by having them specify computations in a high-level form, from which efficient parallel programs are automatically synthesized.@pqdt@break@The goal of this research is to develop an optimization framework to derive high-performance implementations for a set of given tensor contractions. In particular, the issues investigated include: (1) Development of an efficient in-memory parallel algorithm for a tensor contraction: A tensor contraction is essentially a generalized matrix multiplication involving multi-dimensional arrays. A novel parallel tensor contraction algorithm is developed by extending Cannon's memory-efficient parallel matrix multiplication algorithm. (2) Design of a performance-model driven framework for a parallel out-of-core tensor contraction: For a parallel out-of-core tensor contraction, besides the in-core parallel algorithm used, several other factors can affect the overall performance, such as the nested-loop structure (permutation), tile size selection, disk I/O placement and the data partitioning pattern. The best choice here depends on the characteristics of the target machine and the input data. We develop performance models for different parallel out-of-core alternatives and use p

关键词： Computer Science compiler optimization Loop transformations High-performance computing Parallel algorithms Out-of-core algorithms

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Cobalt: A Language for Writing Provably-Sound compiler optimizations

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ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE 2005年第1期132卷 5-17页

作者： Lerner, Sorin Millstein, Todd Chambers, Craig Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA

We overview the current status and future directions of the Cobalt project. Cobalt is a domains-pecific language for implementing compiler optimizations as guarded rewrite rules. Cobalt optimizations operate over a C-like intermediate representation including unstructured control flow, pointers to local variables and dynamically allocated memory, and recursive procedures. The design of Cobalt engenders a natural inductive strategy for proving the soundness of optimizations. This strategy is fully automated by requiring an automatic theorem prover to discharge a small set of simple proof obligations for each optimization. We have written a variety of forward and backward intraprocedural dataflow optimizations in Cobalt, including constant propagation and folding, branch folding, full and partial redundancy elimination, full and partial dead assignment elimination, and simple forms of points-to analysis. The implementation of our soundness-checking strategy employs the Simplify automatic theorem prover, and we have used this implementation to automatically prove the above optimizations correct. An execution engine for Cobalt optimizations is implemented as part of the Whirlwind compiler infrastructure.

关键词： compiler optimization automated correctness proofs

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Automatic Tuning of compiler optimizations and Analysis of their Impact

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Procedia Computer Science 2013年 18卷 1312-1321页

作者： Dmitry Plotnikov Dmitry Melnik Mamikon Vardanyan Ruben Buchatskiy Roman Zhuykov Je-Hyung Lee ISP RAS 25 Alexander Solzhenitsyn st. Moscow 109004 Russia Samsung Electronics Co. Ltd. Next-Generation Computing Lab. Suwon Korea

Modern compilers can work on many platforms and implement a lot of optimizations, which are not always tuned well for every target platform. In the paper we present the Tool for Automatic compiler Tuning (TACT), which helps to identify such underperforming compiler optimizations. Using GCC for ARM, we show how this tool can be used to improve performance of several popular applications, and how the results can be further analyzed to find places for improvement in the GCC compiler itself.

关键词： compiler optimization Automatic Performance Tuning GCC

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SRF Coloring:Stream Register File Allocation via Graph Coloring

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Journal of Computer Science & Technology 2009年第1期24卷 152-164页

作者：杨学军邓宇汪黎晏小波杜静张英王桂彬唐滔 PDL School of ComputerNational University of Defense Technology

Stream Register File （SRF） is a large on-chip memory of the stream processor and its efficient management is essential for good performance. Current stream programming languages expose the management of SRF to the programmer, incurring heavy burden on the programmer and bringing difficulties to inheriting the legacy codes. SF95 is the language developed for FT64 which is the first 64-bit stream processor designed for scientific applications. SF95 conceals SRF from the programmer and leaves the management of SRF to its compiler. In this paper, we present a compiler approach named SRF Coloring to manage SRF automatically. The novelties of this paper are： first, it is the first time to use the graph coloring-based algorithm for the SRF management; second, an algorithm framework for SRF Coloring that is well suited to the FT64 architecture is proposed this framework is based on a well-understood graph coloring algorithm for register allocation, together with some modifications to deal with the unusual aspects of SRF problem; third, the SRF Coloring algorithm is implemented in SF95compiler, a compiler designed for FT64 and SF95. The experimental results show that our approach represents a practical and promising solution to SRF allocation.

关键词： memory management SRF coloring graph coloring stream processor stream register file compiler optimization

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HEURISTIC ALGORITHMS FOR REGISTER ALLOCATION

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IEE PROCEEDINGS-E COMPUTERS AND DIGITAL TECHNIQUES 1992年第1期139卷 73-80页

作者： LUQUE, E RIPOLL, A DIEZ, T Department d'Informática Universidad Autónoma de Barcelona Barcelona Spain

A model to find the optimal register allocation of program variables, including large strongly connected regions (loops), is presented. The program is represented by a directed control flow graph and assumes that all variables have been allocated to memory locations. The model takes into account the cost and saving times involved in allocating variables to registers in each node of the program in order to maximise the global time saving in program execution. As the solution of this model requires exponential resources (both time and space) in the worse case, two heuristic algorithms with O(m) and O(m2) complexities have been developed. Comparisons between the performance of the two heuristic algorithms and the optimal one are made for a set of representative programs.

关键词： REGISTER ALLOCATION INTEGER LINEAR PROGRAMMING TIME SAVING compiler optimization

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GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices Based on Fine-Grained Structured Weight Sparsity

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第10期44卷 6224-6239页

作者： Niu, Wei Li, Zhengang Ma, Xiaolong Dong, Peiyan Zhou, Gang Qian, Xuehai Lin, Xue Wang, Yanzhi Ren, Bin William & Mary Dept Comp Sci Williamsburg VA 23185 USA Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Univ Southern Calif Dept Comp Sci Los Angeles CA 90089 USA

It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices, because even the powerful modern mobile devices are considered as "resource-constrained" when executing large-scale DNNs. It necessitates the sparse model inference via weight pruning, i.e., DNN weight sparsity, and it is desirable to design a new DNN weight sparsity scheme that can facilitate real-time inference onmobile devices while preserving a high sparse model accuracy. This paper designs a novel mobile inference acceleration framework GRIM that is General to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and that achieves Real-time execution and high accuracy, leveraging fine-grained structured sparse model Inference and compiler optimizations for Mobiles. We start by proposing a new fine-grained structured sparsity scheme through the Block-based Column-Row (BCR) pruning. Based on this new fine-grained structured sparsity, our GRIM framework consists of two parts: (a) the compiler optimization and code generation for real-time mobile inference;and (b) the BCR pruning optimizations for determining pruning hyperparameters and performing weight pruning. We compare GRIM with Alibaba MNN, TVM, TensorFlow-Lite, a sparse implementation based on CSR, PatDNN, and ESE (a representative FPGA inference acceleration framework for RNNs), and achieve up to 14:08x speedup.

关键词： compiler optimization model compression real-time inference deep neural networks mobile computing

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Time optimal software pipelining of loops with control flows

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2003年第5期31卷 339-391页

作者： Yun, HS Kim, J Moon, SM Seoul Natl Univ Sch Engn & Comp Sci Seoul 151742 South Korea Seoul Natl Univ Sch Elect Engn Seoul 151742 South Korea

Software pipelining is widely used as a compiler optimization technique to achieve high performance in machines that exploit instruction-level parallelism. However, surprisingly, there have been few theoretical or empirical results on time optimal software pipelining of loops with control flows. In this paper, we present three new theoretical and practical contributions for this under-investigated problem. First, we propose a necessary and sufficient condition for a loop with control flows to have an optimally software-pipelined program. We also present a decision procedure to compute the condition. As part of the formal treatment of software pipelining, we propose a new formalization of software pipelining. Second, we present two software pipelining algorithms. The first algorithm computes an optimal solution for every loop satisfying the condition, but may run in exponential time. The second algorithm computes optimal solutions efficiently for most ( but not all) loops satisfying the condition. The former one proves the sufficiency of the condition and the latter one suggests a practical optimal software pipelining algorithm. Third, we present experimental results which strongly indicate that achieving the time optimality in the software-pipelined programs is a viable goal in practice with reasonable hardware support.

关键词： software pipelining instruction-level parallelism VLIW compiler optimization

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Empirical performance model-driven data layout optimization selection for tensor contraction expressions

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2012年第3期72卷 338-352页

作者： Lu, Qingda Gao, Xiaoyang Krishnamoorthy, Sriram Baumgartner, Gerald Ramanujam, J. Sadayappan, P. Louisiana State Univ Dept Comp Sci Baton Rouge LA 70803 USA Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Louisiana State Univ Dept Elect & Comp Engn Baton Rouge LA 70803 USA

Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined in ATLAS by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance models used. In this paper, we describe an approach where a class of computations is modeled in terms of constituent operations that are empirically measured, thereby allowing modeling of the overall execution time. The performance model with empirically determined cost components is used to select library calls and choose data layout transformations in the context of the Tensor Contraction Engine, a compiler for a high-level domain-specific language for expressing computational models in quantum chemistry. The effectiveness of the approach is demonstrated through experimental measurements on representative computations from quantum chemistry. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Data layout optimization Library call selection compiler optimization Tensor contractions

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Optimizing the performance of sensor network programs through estimation-based code profiling

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PERVASIVE AND MOBILE COMPUTING 2017年 42卷 285-298页

作者： Wan, Lipeng Cao, Qing Zhou, Wenjun Univ Tennessee Knoxville TN 37996 USA

With the development of sensor technology and embedded systems, building large-scale, low-cost sensor networks, which is a critical step to facilitating the application of pervasive sensing in the future, becomes possible. One of the major challenges in developing sensor network applications is to improve the execution efficiency of programs running on power-constrained embedded devices. While profiling-guided code optimization has been widely used as a compiler-level optimization technique for improving the performance of programs running on general-purpose computers, it has not been applied to sensor network programs due to some defects. In this paper, we overcome these defects and design a more effective profiling-guided code placement approach for sensor network programs. Specifically, we model the execution of sensor network programs taking nondeterministic inputs as discrete-time Markov processes, and propose a novel approach named Code Tomography to estimate parameters of the Markov models that reflect sensor network programs' dynamic execution behaviors by only using end-to-end timing information measured at the start and end points of each procedure in the source code. The parameters estimated by Code Tomography are fed back to compilers to optimize the code placement. The evaluation results demonstrate that Code Tomography can achieve satisfactory estimation accuracy with low profiling overhead and the branch misprediction rate can be reduced after reorganizing the code placement based on the profiling results. Besides, Code Tomography can also be useful for purposes such as post-mortem analysis, debugging and energy profiling of sensor network programs. (C) 2017 Published by Elsevier B.V.

关键词： Code profiling compiler optimization Sensor network program

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