|
| This Article | ||
| ||
| Share | ||
| Bibliographic References | ||
| Add to: | ||
 Digg  Furl  Spurl  Blink  Simpy  Google  Del.icio.us  Y!MyWeb | ||
| Search | ||
| ||
| ASCII Text | x | ||
| Jeffrey S. Vetter, Richard Glassbrook, Jack Dongarra, Karsten Schwan, Bruce Loftis, Stephen McNally, Jeremy Meredith, James Rogers, Philip Roth, Kyle Spafford, Sudhakar Yalamanchili, "Keeneland: Bringing Heterogeneous GPU Computing to the Computational Science Community," Computing in Science and Engineering, vol. 13, no. 5, pp. 90-95, September/October, 2011. | |||
| BibTex | x | ||
| @article{ 10.1109/MCSE.2011.83, author = {Jeffrey S. Vetter and Richard Glassbrook and Jack Dongarra and Karsten Schwan and Bruce Loftis and Stephen McNally and Jeremy Meredith and James Rogers and Philip Roth and Kyle Spafford and Sudhakar Yalamanchili}, title = {Keeneland: Bringing Heterogeneous GPU Computing to the Computational Science Community}, journal ={Computing in Science and Engineering}, volume = {13}, number = {5}, issn = {1521-9615}, year = {2011}, pages = {90-95}, doi = {http://doi.ieeecomputersociety.org/10.1109/MCSE.2011.83}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - MGZN JO - Computing in Science and Engineering TI - Keeneland: Bringing Heterogeneous GPU Computing to the Computational Science Community IS - 5 SN - 1521-9615 SP90 EP95 EPD - 90-95 A1 - Jeffrey S. Vetter, A1 - Richard Glassbrook, A1 - Jack Dongarra, A1 - Karsten Schwan, A1 - Bruce Loftis, A1 - Stephen McNally, A1 - Jeremy Meredith, A1 - James Rogers, A1 - Philip Roth, A1 - Kyle Spafford, A1 - Sudhakar Yalamanchili, PY - 2011 KW - High-performance computing KW - heterogeneous processors KW - GPU KW - Graphics processor KW - computational science KW - emerging architectures VL - 13 JA - Computing in Science and Engineering ER - | |||
The Keeneland project's goal is to develop and deploy an innovative, GPU-based high-performance computing system for the NSF computational science community.
1. J. Nickolls and W.J. Dally, "The GPU Computing Era," IEEE Micro, vol. 30, no. 2, 2010, pp. 56–69.
2. L. Seiler et al., "Larrabee: A Many-Core x86 Architecture for Visual Computing," ACM Trans. Graphics, vol. 27, no. 3, 2008, pp. 1–15.
3. J.D. Owens et al., "A Survey of General-Purpose Computation on Graphics Hardware," Computer Graphics Forum, vol. 26, no. 1, 2007, pp. 80–113.
4. M. Pharr and R. Fernando, GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation (GPU Gems), Addison-Wesley Professional, 2005.
5. W.R. Mark et al., "Cg: A System for Programming Graphics Hardware in a C-Like Language," ACM Trans. Graphics, vol. 22, no. 3, 2003, pp. 896–907.
6. J. Nickolls and I. Buck, "Nvidia CUDA Software and GPU Parallel Computing Architecture," Proc. Microprocessor Forum, 2007.
7. Khronos Group, OpenCL—The Open Standard for Parallel Programming of Heterogeneous Systems, 2008, www.khronos.orgopencl.
8. J.E. Stone, D. Gohara, and G. Shi, "OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems," Computing in Science and Eng., vol. 12, no. 3, 2010, pp. 66–73.
9. K. Spafford, J. Meredith, and J. Vetter, "Quantifying NUMA and Contention Effects in Multi-GPU Systems," Proc. ACM 4th Workshop General Purpose Computation on Graphics Processors, ACM Press, 2011; doi:10.1145/1964179.1964194.
10. H. Ltaief et al., "A Scalable High Per-formant Cholesky Factorization for Multicore with GPU Accelerators," High Performance Computing for Computational Science–VECPAR, Springer-Verlag, 2010, pp. 93–101.
11. A. Kerr, G. Diamos, and S. Yalamanchili, "A Characterization and Analysis of PTX Kernels," Proc. IEEE Int'l Symp. Workload Characterization, IEEE CS Press, 2009, pp. 3–12.
12. A.M. Merritt et al., "Shadowfax: Scaling in Heterogeneous Cluster Systems via GPGPU Assemblies," Proc. 5th Int'l Workshop Virtualization Technologies in Distributed Computing, ACM Press, 2011, pp. 3–10.
13. A. Danalis et al., "The Scalable Heterogeneous Computing (SHOC) Benchmark Suite," ACM Workshop General-Purpose Computation on Graphics Processing Units (GPGPU), ACM Press, 2010, pp. 63–74.
14. E. Lindahl, B. Hess, and D. van der Spoel, "Gromacs 3.0: A Package for Molecular Simulation and Trajectory Analysis," J. Molecular Modeling, vol. 7, no. 8, 2001, pp. 306–17.
15. J.C. Phillips et al., "Scalable Molecular Dynamics with NAMD," J. Computing in Chemistry, vol. 26, no. 16, 2005, pp. 1781–1802.
16. A. Rahimian et al., "Petascale Direct Numerical Simulation of Blood Flow on 200K Cores and Heterogeneous Architectures (Gordon Bell Award Winner)," Proc. Int'l Conf. High Performance Computing, Networking, Storage, and Analysis, IEEE CS Press, 2010, pp. 1–11.
17. A. Alexandru et al., "Multi-Mass Solvers for Lattice QCD on GPUs," Actaphysics,29 Mar. 2011; arXiv:1103.5103v1.
18. K. Esler et al., "Fully Accelerating Quantum Monte Carlo Simulations of Real Materials on GPU Clusters," Computing in Science and Eng., vol. 13, no. 5, 2011.
19. G. Khanna and J. McKennon, "Numerical Modeling of Gravitational Wave Sources Accelerated by OpenCL," Computer Physics Comm., vol. 181, no. 9, 2010, pp. 1605–1611.