Alok Mishra
ABSTRACT
The latest OpenMP standard offers automatic device offloading capabilities which facilitate GPU programming. Despite this, there remain many challenges. One of these is the unified memory feature introduced in recent GPUs. GPUs in current and future HPC systems have enhanced support for unified memory space. In such systems, CPU and GPU can access each other's memory transparently, that is, the data movement is managed automatically by the underlying system software and hardware. Memory over subscription is also possible in these systems. However, there is a significant lack of knowledge about how this mechanism will perform, and how programmers should use it. We have modified several benchmarks codes, in the Rodinia benchmark suite, to study the behavior of OpenMP accelerator extensions and have used them to explore the impact of unified memory in an OpenMP context. We moreover modified the open source LLVM compiler to allow OpenMP programs to exploit unified memory. The results of our evaluation reveal that, while the performance of unified memory is comparable with that of normal GPU offloading for benchmarks with little data reuse, it suffers from significant overhead when GPU memory is over subcribed for benchmarks with large amount of data reuse. Based on these results, we provide several guidelines for programmers to achieve better performance with unified memory.
- Oak Ridge Leadership Computing Facility - Summit. https://www.olcf.ornl.gov/summitGoogle Scholar
- OpenACC. http://www.openacc.orgGoogle Scholar
- 2013. OpenMP 4.0 Specifications. (2013). http://www.openmp.org/wp-content/uploads/OpenMP4.0.0.pdfGoogle Scholar
- Samuel F. Antao, Alexey Bataev, Arpith C. Jacob, Gheorghe-Teodor Bercea, Alexandre E. Eichenberger, Georgios Rokos, Matt Martineau, Tian Jin, Guray Ozen, Zehra Sura, Tong Chen, Hyojin Sung, Carlo Bertolli, and Kevin O'Brien. 2016. Offloading Support for OpenMP in Clang and LLVM. In Proceedings of the Third Workshop on LLVM Compiler Infrastructure in HPC (LLVM-HPC '16). IEEE Press, Piscataway, NJ, USA, 1--11. https://doi.org/10.1109/LLVM-HPC.2016.6 Google ScholarCross Ref
- Gheorghe-Teodor Bercea, Carlo Bertolli, Samuel F. Antao, Arpith C. Jacob, Alexandre E. Eichenberger, Tong Chen, Zehra Sura, Hyojin Sung, Georgios Rokos, David Appelhans, and Kevin O'Brien. 2015. Performance Analysis of OpenMP on a GPU Using a CORAL Proxy Application. In Proceedings of the 6th International Workshop on Performance Modeling, Benchmarking, and Simulation of High Performance Computing Systems (PMBS '15). ACM, New York, NY, USA, Article 2, 11 pages. https://doi.org/10.1145/2832087.2832089Google ScholarDigital Library
- Shuai Che, Michael Boyer, Jiayuan Meng, David Tarjan, Jeremy W Sheaffer, Sang-Ha Lee, and Kevin Skadron. 2009. Rodinia: A benchmark suite for heterogeneous computing. In Workload Characterization, 2009. IISWC 2009. IEEE International Symposium on. IEEE, 44--54.Google ScholarDigital Library
- Shuai Che, Jeremy W Sheaffer, Michael Boyer, Lukasz G Szafaryn, Liang Wang, and Kevin Skadron. 2010. A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads. In Workload Characterization (IISWC), 2010 IEEE International Symposium on. IEEE, 1--11.Google ScholarDigital Library
- L. Dagum and R. Menon. 1998. OpenMP: an industry standard API for shared-memory programming. IEEE Computational Science and Engineering 5, 1 (Jan 1998), 46--55. https://doi.org/10.1109/99.660313 Google ScholarDigital Library
- Anthony Danalis, Gabriel Marin, Collin McCurdy, Jeremy S. Meredith, Philip C. Roth, Kyle Spafford, Vinod Tipparaju, and Jeffrey S. Vetter. 2010. The Scalable Heterogeneous Computing (SHOC) Benchmark Suite. In Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-3). ACM, New York, NY, USA, 63--74. https://doi.org/10.1145/1735688.1735702 Google ScholarDigital Library
- Scott Grauer-Gray, Lifan Xu, Robert Searles, Sudhee Ayalaso-mayajula, and John Cavazos. 2012. Auto-tuning a high-level language targeted to GPU codes. In Innovative Parallel Computing (InPar), 2012. IEEE, 1--10. Google ScholarCross Ref
- Thomas B. Jablin, Prakash Prabhu, James A. Jablin, Nick P. Johnson, Stephen R. Beard, and David I. August. 2011. Automatic CPU-GPU Communication Management and Optimization. In Proceedings of the 32Nd ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI '11). ACM, New York, NY, USA, 142--151. https://doi.org/10.1145/1993498.1993516 Google ScholarDigital Library
- Aamer Jaleel, Kevin B. Theobald, Simon C. Steely, Jr., and Joel Emer. 2010. High Performance Cache Replacement Using Re-reference Interval Prediction (RRIP). In Proceedings of the 37th Annual International Symposium on Computer Architecture (ISCA '10). ACM, New York, NY, USA, 60--71. https://doi.org/10.1145/1815961.1815971 Google ScholarDigital Library
- Hao-Qiang Jin, Michael Frumkin, and Jerry Yan. 1999. The OpenMP implementation of NAS parallel benchmarks and its performance. (1999).Google Scholar
- Chris Lattner and Vikram Adve. 2004. LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation. In Proceedings of the International Symposium on Code Generation and Optimization: Feedback-directed and Runtime Optimization (CGO '04). IEEE Computer Society, Washington, DC, USA, 75--. http://dl.acm.org/citation.cfm?id=977395.977673 Google ScholarCross Ref
- Lingda Li, Dong Tong, Zichao Xie, Junlin Lu, and Xu Cheng. 2012. Optimal Bypass Monitor for High Performance Last-level Caches. In Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques (PACT '12). ACM, New York, NY, USA, 315--324. https://doi.org/10.1145/2370816.2370862 Google ScholarDigital Library
- M. Martineau, S. McIntosh-Smith, C. Bertolli, A. C. Jacob, S. F. Antao, A. Eichenberger, G. T. Bercea, T. Chen, T. Jin, K. O'Brien, G. Rokos, H. Sung, and Z. Sura. 2016. Performance Analysis and Optimization of Clang's OpenMP 4.5 GPU Support. In 2016 7th International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS). 54--64. https://doi.org/10.1109/PMBS.2016.011Google Scholar
- M. Martineau, S. McIntosh-Smith, and W. Gaudin. 2016. Evaluating OpenMP 4.0's Effectiveness as a Heterogeneous Parallel Programming Model. In 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 338--347. https://doi.org/10.1109/IPDPSW.2016.70Google ScholarCross Ref
- Perhaad Mistry, Yash Ukidave, Dana Schaa, and David Kaeli. 2013. Valar: A Benchmark Suite to Study the Dynamic Behavior of Heterogeneous Systems. In Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units (GPGPU-6). ACM, New York, NY, USA, 54--65. https://doi.org/10.1145/2458523.2458529 Google ScholarDigital Library
- NVIDIA. 2007. Compute unified device architecture programming guide. (2007).Google Scholar
- Sreepathi Pai, R. Govindarajan, and Matthew J. Thazhuthaveetil. 2012. Fast and Efficient Automatic Memory Management for GPUs Using Compiler-assisted Runtime Coherence Scheme. In Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques (PACT '12). ACM, New York, NY, USA, 33--42. https://doi.org/10.1145/2370816.2370824Google Scholar
- Moinuddin K. Qureshi, Aamer Jaleel, Yale N. Patt, Simon C. Steely, and Joel Emer. 2007. Adaptive Insertion Policies for High Performance Caching. In Proceedings of the 34th Annual International Symposium on Computer Architecture (ISCA '07). ACM, New York, NY, USA, 381--391. https://doi.org/10.1145/1250662.1250709 Google ScholarDigital Library
- John E Stone, David Gohara, and Guochun Shi. 2010. OpenCL: A parallel programming standard for heterogeneous computing systems. Computing in science & engineering 12, 3 (2010), 66--73.Google Scholar
- John A Stratton, Christopher Rodrigues, I-Jui Sung, Nady Obeid, Li-Wen Chang, Nasser Anssari, Geng Daniel Liu, and Wen-Mei W Hwu. 2012. Parboil: A revised benchmark suite for scientific and commercial throughput computing. Center for Reliable and High-Performance Computing 127 (2012).Google Scholar
- Yash Ukidave, Fanny Nina Paravecino, Leiming Yu, Charu Kalra, Amir Momeni, Zhongliang Chen, Nick Materise, Brett Daley, Perhaad Mistry, and David Kaeli. 2015. NUPAR: A Benchmark Suite for Modern GPU Architectures. In Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering (ICPE '15). ACM, New York, NY, USA, 253--264. https://doi.org/10.1145/2668930.2688046Google ScholarDigital Library
Recommendations
Characterizing CUDA Unified Memory (UM)-Aware MPI Designs on Modern GPU Architectures
GPGPU '19: Proceedings of the 12th Workshop on General Purpose Processing Using GPUsThe CUDA Unified Memory (UM) interface enables a significantly simpler programming paradigm and has the potential to fundamentally change the way programmers write CUDA applications in the future. Although UM leads to high productivity in programming ...
On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing
SAAHPC '11: Proceedings of the 2011 Symposium on Application Accelerators in High-Performance ComputingThe graphics processing unit (GPU) has made significant strides as an accelerator in parallel computing. However, because the GPU has resided out on PCIe as a discrete device, the performance of GPU applications can be bottlenecked by data transfers ...
Implementing OpenMP’s SIMD Directive in LLVM’s GPU Runtime
ICPP '23: Proceedings of the 52nd International Conference on Parallel ProcessingGPUs support three levels of parallelism: thread blocks, warps (or wavefronts) within a block, and threads within a warp. Some GPU programming models allow the use of all three of these levels, such as OpenMP offloading with the teams, parallel, and simd ...
Comments