Performance Exploration Through Optimistic Static Program Annotations

Doerfert, Johannes; Homerding, Brian; Finkel, Hal

doi:10.1007/978-3-030-20656-7_13

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…More recently, Doerfert et al [9] presented an automated tool to measure the effect that missing static information has on a program. However, their approach is based on incrementally including certain program annotations into the code, and these are often compiler specific.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Auto-Vectorizing Compilers through Objective Withdrawal of Useful Information

Siso

Armour

Thiyagalingam

2019

ACM Trans. Archit. Code Optim.

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The need for compilers to generate highly vectorized code is at an all-time high with the increasing vectorization capabilities of modern processors. To this end, the information that compilers have at their disposal, either through code analysis or via user annotations, is instrumental for auto-vectorization, and hence for the overall performance. However, the information that is available to compilers at compile time and its accuracy varies greatly, as does the resulting performance of vectorizing compilers. Benchmarks like the Test Suite for Vectorizing Compilers (TSVC) have been developed to evaluate the vectorization capability of such compilers. The overarching approach of TSVC and similar benchmarks is to evaluate the compilers under the best possible scenario (i.e., assuming that compilers have access to all useful contextual information at compile time). Although this idealistic view is useful to observe the capability of compilers for auto-vectorization, it is not a true reflection of the conditions found in real-world applications. In this article, we propose a novel method for evaluating the auto-vectorization capability of compilers. Instead of assuming that compilers have access to a wealth of information at compile time, we formulate a method to objectively supply or withdraw information that would otherwise aid the compiler in the autovectorization process. This method is orthogonal to the approach adopted by TSVC, and as such, it provides the means of assessing the capabilities of modern vectorizing compilers in a more detailed way. Using this new method, we exhaustively evaluated five industry-grade compilers (GNU, Intel, Clang, PGI, and IBM) on four representative vector platforms (AVX-2, AVX-512 (Skylake), AVX-512 (KNL), and AltiVec) using the modified version of TSVC and application-level proxy kernels. The results show the impact that withdrawing information has on the vectorization capabilities of each compiler and also prove the validity of the presented technique. CCS Concepts: • Software and its engineering → Compilers;

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Section: Related Workmentioning

confidence: 99%

Evaluating Auto-Vectorizing Compilers through Objective Withdrawal of Useful Information

Siso

Armour

Thiyagalingam

2019

ACM Trans. Archit. Code Optim.

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ORAQL — Optimistic Responses to Alias Queries in LLVM

Hueckelheim,

Doerfert

2023

Proceedings of the 52nd International Conference on Parallel Processing

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Refined Input, Degraded Output: The Counterintuitive World of Compiler Behavior

Theodoridis,

2024

Proc. ACM Program. Lang.

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To optimize a program, a compiler needs precise information about it. Significant effort is dedicated to improving the ability of compilers to analyze programs, with the expectation that more information results in better optimization. But this assumption does not always hold: due to unexpected interactions between compiler components and phase ordering issues, sometimes more information leads to worse optimization. This can lead to wasted research and engineering effort whenever compilers cannot efficiently leverage additional information. In this work, we systematically examine the extent to which additional information can be detrimental to compilers. We consider two types of information: dead code, i.e., whether a program location is unreachable, and value ranges, i.e., the possible values a variable can take at a specific program location. Given a seed program, we refine it with additional information and check whether this degrades the output. Based on this approach, we develop a fully automated and effective testing method for identifying such issues, and through an extensive evaluation and analysis, we quantify their existence and prevalence in widely used compilers. In particular, we have reported 59 cases in GCC and LLVM, of which 55 have been confirmed or fixed so far, highlighting the practical relevance and value of our findings. This work’s fresh perspective opens up a new direction in understanding and improving compilers.

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Performance Exploration Through Optimistic Static Program Annotations

Cited by 3 publications

References 20 publications

Evaluating Auto-Vectorizing Compilers through Objective Withdrawal of Useful Information

Evaluating Auto-Vectorizing Compilers through Objective Withdrawal of Useful Information

ORAQL — Optimistic Responses to Alias Queries in LLVM

Refined Input, Degraded Output: The Counterintuitive World of Compiler Behavior

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