The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Auto-tuning a high-level language targeted to GPU codes
Abstract
Determining the best set of optimizations to apply to a kernel to be executed on the graphics processing unit (GPU) is a challenging problem. There are large sets of possible optimization configurations that can be applied, and many applications have multiple kernels. Each kernel may require a specific configuration to achieve the best performance, and moving an application to new hardware often requires a new optimization configuration for each kernel. In this work, we apply optimizations to GPU code using HMPP, a high-level directive-based language and source-to-source compiler that can generate CUDA / OpenCL code. However, programming with high-level languages may mean a loss of performance compared to using low-level languages. Our work shows that it is possible to improve the performance of a high-level language by using auto-tuning. We perform auto-tuning on a large optimization space on GPU kernels, focusing on loop permutation, loop unrolling, tiling, and specifying which loop(s) to parallelize, and show results on convolution kernels, codes in the PolyBench suite, and an implementation of belief propagation for stereo vision. The results show that our auto-tuned HMPP-generated implementations are significantly faster than the default HMPP implementation and can meet or exceed the performance of manually coded CUDA / OpenCL implementations.
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Cites
Efficient Belief Propagation for Early Vision
Felzenszwalb, P. F., & Huttenlocher, D. P. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7899-4 · Cited by 1,345
Automated empirical optimizations of software and the ATLAS project
Clint Whaley, R., Petitet, A., & Dongarra, J. J. (2001) · Parallel Computing · doi:10.1016/s0167-8191(00)00087-9 · Cited by 1,317
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SPIRAL: Code Generation for DSP Transforms
Puschel, M., Moura, J. M. F., Johnson, J. R., Padua, D., Veloso, M. M., Singer, B. W., Xiong, J., Franchetti, F., Gacic, A., Voronenko, Y., et al. (2005) · Proceedings of the IEEE · doi:10.1109/jproc.2004.840306 · Cited by 826
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
Bolz, J., Farmer, I., Grinspun, E., & Schröder, P. (2005) · ACM SIGGRAPH 2005 Courses on - SIGGRAPH '05 · doi:10.1145/1198555.1198781 · Cited by 741
SuperLU_DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems
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Sparse matrix solvers on the GPU: conjugate gradients and multigrid
Bolz, J., Farmer, I., Grinspun, E., & Schröder, P. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882364 · Cited by 540
OSKI: A library of automatically tuned sparse matrix kernels
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A fast Fourier transform compiler
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Dutta, A., Alcaraz, J., TehraniJamsaz, A., Cesar, E., Sikora, A., & Jannesari, A. (2023) · Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing · doi:10.1145/3588195.3592984 · Cited by 16
Efficient Intra-Rack Resource Disaggregation for HPC Using Co-Packaged DWDM Photonics
Michelogiannakis, G., Arafa, Y., Cook, B., Dai, L. Y., Hameed Badawy, A.-H., Glick, M., Wang, Y., Bergman, K., & Shalf, J. (2023) · 2023 IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster52292.2023.00021 · Cited by 13
Metadata
- Authors
- Grauer-Gray, S., Xu, L., Searles, R., Ayalasomayajula, S., & Cavazos, J.
- Venue
- 2012 Innovative Parallel Computing (InPar)
- Publisher
- IEEE
- Published
- 2012-05
- Pages
- 1-10
- OpenAlex
- W2128120785
- Cited by
- 470
- References
- 33
- FWCI
- 15.52
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Grauer-Gray, S., Xu, L., Searles, R., Ayalasomayajula, S., & Cavazos, J. (2012). Auto-tuning a high-level language targeted to GPU codes. 2012 Innovative Parallel Computing (InPar). https://doi.org/10.1109/inpar.2012.6339595
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