Abstract
Graphics processing units (GPUs) have become increasingly popular accelerators in supercomputers, and this trend is likely to continue. With its disruptive architecture and a variety of optimization options, it is often desirable to understand the dynamics between potential application transformations and potential hardware features when designing future GPUs for scientific workloads. However, current codesign efforts have been limited to manual investigation of benchmarks on microarchitecture simulators, which is labor-intensive and time-consuming. As a result, system designers can explore only a small portion of the design space. In this paper, we propose SESH framework, a model-driven codesign framework for GPU, that is able to automatically search the design space by simultaneously exploring prospective application and hardware implementations and evaluate potential software-hardware interactions.
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References
Meng, J., Wu, X., Morozov, V.A., Vishwanath, V., Kumaran, K., Taylor, V., Lee, C.-W.: SKOPE: A Framework for Modeling and Exploring Workload Behavior. Argonne National Laboratory, Tech. Rep. (2012)
Meng, J., Morozov, V., Kumaran, K., Vishwanath, V., Uram, T.: GROPHECY: GPU Performance Projection from CPU Code Skeletons. In: SC 2011 (2011)
Sim, J.W., Dasgupta, A., Kim, H., Vuduc, R.: GPUPerf: A Performance Analysis Framework for Identifying Performance Benefits in GPGPU Applications. In: PPoPP 2012 (2012)
Lim, J., Lakshminarayana, N., Kim, H., Song, W., Yalamanchili, S., Sung, W.: Power Modeling for GPU Architecture using McPAT. Georgia Institute of Technology. Tech. Rep. (2013)
Li, S., Ahn, J.H., Strong, R.D., Brockman, J.B., Tullsen, D.M., Jouppi, N.P.: McPAT: an integrated power, area, and timing modeling framework for multicore and manycore architectures. In: MICRO 42
Song, W.J., Cho, M., Yalamanchili, S., Mukhopadhyay, S., Rodrigues, A.F.: Energy introspector: Simulation infrastructure for power, temperature, and reliability modeling in manycore processors. In: SRC TECHCHON 2011 (2011)
MacSim, http://code.google.com/p/macsim/
Hong, S., Kim, H.: IPP: An Integrated GPU Power and Performance Model that Predicts Optimal Number of Active Cores to Save Energy at Static Time. In: ISCA 2010 (2010)
Linderman, M.D., Collins, J.D., Wang, H., Meng, T.H.: Merge: a programming model for heterogeneous multi-core systems. In: ASPLOS XIII
Che, S., Boyer, M., Meng, J., Tarjan, D., Sheaffer, J.W., Skadron, K.: A performance study of general-purpose applications on graphics processors using CUDA. Journal of Parallel and Distributed Computing (2008)
Pieper, S.C., Wiringa, R.B.: Quantum Monte Carlo calculations of light nuclei. Annu. Rev. Nucl. Part. Sci. 51, 53 (2001)
Pieper, S.C., Varga, K., Wiringa, R.B.: Quantum Monte Carlo calculations of A=9,10 nuclei. Phys. Rev. C 66, 044310-1:14 (2002)
NVIDIA Corporation: GeForce GTX 280 specifications (2008), http://www.nvidia.com/object/product_geforce_gtx_280_us.html
NVIDIA, Fermi: Nvidia’s next generation cuda compute architecture, http://www.nvidia.com/fermi
NVIDIA’s next generation CUDA compute architecture: Kepler GK110. NVIDIA Corporation (2012)
Jablin, T.B., Prabhu, P., Jablin, J.A., Johnson, N.P., Beard, S.R., August, D.I.: Automatic CPU-GPU communication management and optimization. In: PLDI 2011 (2011)
Jablin, T.B., Jablin, J.A., Prabhu, P., Liu, F., August, D.I.: Dynamically managed data for cpu-gpu architectures. In: CGO 2012 (2012)
Spafford, K., Vetter, J.: Aspen: A domain specific language for performance modeling. In: SC 2012 (2012)
Williams, S., Waterman, A., Patterson, D.: Roofline: an insightful visual performance model for multicore architectures. Communications of the ACM - A Direct Path to Dependable Software (2009)
Valiant, L.G.: A bridging model for parallel computation. Commun. ACM (1990)
Karp, R.M., Ramachandran, V.: A survey of parallel algorithms for shared-memory machines. EECS Department, University of California, Berkeley, Tech. Rep. (1988)
Valluri, M., John, L.: Is Compiling for Performance == Compiling for Power. In: INTERACT-5
Brandolese, C., Fornaciari, W., Salice, F., Sciuto, D.: The Impact of Source Code Transformations on Software Power and Energy Consumption. Journal of Circuits, Systems, and Computers (2002)
Joseph, R., Martonosi, M.: Run-time power estimation in high performance microprocessors. In: ISLPED 2001 (2001)
Bellosa, F., Kellner, S., Waitz, M., Weissel, A.: Event-driven energy accounting for dynamic thermal management. In: COLP 2003 (2003)
Wu, W., Jin, L., Yang, J., Liu, P., Tan, S.-D.: A systematic method for functional unit power estimation in microprocessors. In: DAC 2006 (2006)
Peddersen, J., Parameswaran, S.: CLIPPER: Counter-based Low Impact Processor Power Estimation at Run-time. In: ASP-DAC 2007 (2007)
Powell, M., Biswas, A., Emer, J., Mukherjee, S., Sheikh, B., Yardi, S.: CAMP: A technique to estimate per-structure power at run-time using a few simple parameters. In: HPCA 2009 (2009)
Jacobson, H., Buyuktosunoglu, A., Bose, P., Acar, E., Eickemeyer, R.: Abstraction and microarchitecture scaling in early-stage power modeling. In: HPCA 2011 (2011)
Czechowski, K., Vuduc, R.: A theoretical framework for algorithm-architecture co-design. In: IPDPS 2013 (2013)
Wang, G.: Power analysis and optimizations for GPU architecture using a power simulator. In: ICACTE 2010 (2010)
Ramani, K., Ibrahim, A., Shimizu, D.: PowerRed: A Flexible Power Modeling Framework for Power Efficiency Exploration in GPUs. In: GPGPU 2007 (2007)
Ma, X., Dong, M., Zhong, L., Deng, Z.: Statistical Power Consumption Analysis and Modeling for GPU-based Computing. In: HotPower 2009 (2009)
Nagasaka, H., Maruyama, N., Nukada, A., Endo, T., Matsuoka, S.: Statistical power modeling of GPU kernels using performance counters. In: IGCC 2010 (2010)
Chen, J., Li, B., Zhang, Y., Peng, L., kwon Peir, J.: Tree structured analysis on gpu power study. In: ICCD 2011 (2011)
Zhang, Y., Hu, Y., Li, B., Peng, L.: Performance and Power Analysis of ATI GPU: A Statistical Approach. In: NAS 2011 (2011)
Wu, W., Lee, B.C.: Inferred Models for Dynamic and Sparse Hardware-Software Spaces. In: MICRO 2012 (2012)
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Lee, J.H., Meng, J., Kim, H. (2014). SESH Framework: A Space Exploration Framework for GPU Application and Hardware Codesign. In: Jarvis, S., Wright, S., Hammond, S. (eds) High Performance Computing Systems. Performance Modeling, Benchmarking and Simulation. PMBS 2013. Lecture Notes in Computer Science(), vol 8551. Springer, Cham. https://doi.org/10.1007/978-3-319-10214-6_9
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DOI: https://doi.org/10.1007/978-3-319-10214-6_9
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