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Graph analytics codes are widely used and tend to exhibit input-dependent behavior, making them particularly interesting for software verification and validation. This paper presents Indigo3, a labeled benchmark suite based on 7 graph algorithms that are implemented in different styles, including versions with deliberately planted bugs. We systematically combine 13 sets of implementation styles and 15 common bug types to create the 41,790 CUDA, OpenMP, and parallel C programs in the suite. Each code is labeled with the styles and bugs it incorporates. We used 4 subsets of Indigo3 to test 5 program-verification tools. Our results show that the tools perform quite differently across the bug types and implementation styles, have distinct strengths and weaknesses, and generally struggle with graph codes. We discuss the styles and bugs that tend to be the most challenging as well as the programming patterns that yield false positives.
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The Indigo Program-Verification Microbenchmark Suite of Irregular Parallel Code Patterns
Irregular programs are found in many domains and tend to exhibit input-dependent control flow and memory accesses. This paper introduces the Indigo suite of important irregular parallel code patterns for testing verification and other tools. We studied many irregular CPU and GPU programs and extracted the key code patterns. Then, we methodically built variations of these patterns to alter the control-flow and memory-access behavior and/or introduce bugs, yielding the thousands of OpenMP and CUDA microbenchmarks in the suite. Indigo includes a set of generators to systematically create an unbounded number of inputs for each microbenchmark, which is essential to exercise the wide range of possible behaviors of input-dependent codes. To manage the millions of code and input combinations, Indigo provides the flexibility to generate user-defined subsets of the suite. Experiments with a subset of buggy and bug-free codes illustrate that irregular programs pose a significant challenge to both static and dynamic program verification tools. Moreover, such tools can perform quite differently across code patterns that contain the same bug.
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- Award ID(s):
- 1955367
- PAR ID:
- 10343589
- Date Published:
- Journal Name:
- 2022 IEEE International Symposium on Performance Analysis of Systems and Software
- Page Range / eLocation ID:
- 24 to 34
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISPASS55109.2022.00003
