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We present the design and implementation of GVM, the first system for executing Java bytecode entirely on GPUs. GVM is ideal for applications that execute a large number of short-living tasks, which share a significant fraction of their codebase and have similar execution time. GVM uses novel algorithms, scheduling, and data layout techniques to adapt to the massively parallel programming and execution model of GPUs. We apply GVM to generate and execute tests for Java projects. First, we implement a sequence-based test generation on top of GVM and design novel algorithms to avoid redundant test sequences. Second, we use GVM to execute randomly generated test cases. We evaluate GVM by comparing it with two existing Java bytecode interpreters (Oracle JVM and Java Pathfinder), as well as with the Oracle JVM with just-in-time (JIT) compiler, which has been engineered and optimized for over twenty years. Our evaluation shows that sequence-based test generation on GVM outperforms both Java Pathfinder and Oracle JVM interpreter. Additionally, our results show that GVM performs as well as running our parallel sequence-based test generation algorithm using JVM with JIT with many CPU threads. Furthermore, our evaluation on several classes from open-source projects shows that executing randomly generated tests on GVM outperforms sequential execution on JVM interpreter and JVM with JIT.
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A fine-grained parallelization of the immersed boundary method
We present new algorithms for the parallelization of Eulerian–Lagrangian interaction operations in the immersed boundary method. Our algorithms rely on two well-studied parallel primitives: key-value sort and segmented reduce. The use of these parallel primitives allows us to implement our algorithms on both graphics processing units (GPUs) and on other shared-memory architectures. We present strong and weak scaling tests on problems involving scattered points and elastic structures. Our tests show that our algorithms exhibit near-ideal scaling on both multicore CPUs and GPUs.
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- Award ID(s):
- 1714844
- PAR ID:
- 10369058
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- The International Journal of High Performance Computing Applications
- Volume:
- 36
- Issue:
- 4
- ISSN:
- 1094-3420
- Format(s):
- Medium: X Size: p. 443-458
- Size(s):
- p. 443-458
- Sponsoring Org:
- National Science Foundation
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