Performance Evolution of GPU versus CPU in Iterative algorithms

High-performance computing is one of the most demanding technologies in today\u27s computational world with a variety of applications such as big data analysis, and solving complex computing algorithm. Engineers have invented multiple technologies such as CPUs, GPUs, GGPUS, FPGAs, clusters and distributed high-performance computational systems for high-performance computing. This research has focused on evaluating GPU and CPU two of the main technologies that could be used in high-performance computing. The researchers have developed a methodology to evaluate the performance of GPU and compare it with CPU under different test subjects. Finally, this research illustrated the power and weaknesses of GPU over the CPU under certain circumstances.https://ecommons.udayton.edu/stander_posters/2229/thumbnail.jp

Experiences with porting and modelling wavefront algorithms on many-core architectures

We are currently investigating the viability of many-core architectures for the acceleration of wavefront applications and this report focuses on graphics processing units (GPUs) in particular. To this end, we have implemented NASA’s LU benchmark – a real world production-grade application – on GPUs employing NVIDIA’s Compute Unified Device Architecture (CUDA). This GPU implementation of the benchmark has been used to investigate the performance of a selection of GPUs, ranging from workstation-grade commodity GPUs to the HPC "Tesla” and "Fermi” GPUs. We have also compared the performance of the GPU solution at scale to that of traditional high perfor- mance computing (HPC) clusters based on a range of multi- core CPUs from a number of major vendors, including Intel (Nehalem), AMD (Opteron) and IBM (PowerPC). In previous work we have developed a predictive “plug-and-play” performance model of this class of application running on such clusters, in which CPUs communicate via the Message Passing Interface (MPI). By extending this model to also capture the performance behaviour of GPUs, we are able to: (1) comment on the effects that architectural changes will have on the performance of single-GPU solutions, and (2) make projections regarding the performance of multi-GPU solutions at larger scale

On the acceleration of wavefront applications using distributed many-core architectures

In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P). Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures

Accelerating the Rate of Astronomical Discovery with GPU-Powered Clusters

In recent years, the Graphics Processing Unit (GPU) has emerged as a low-cost alternative for high performance computing, enabling impressive speed-ups for a range of scientific computing applications. Early adopters in astronomy are already benefiting in adapting their codes to take advantage of the GPU's massively parallel processing paradigm. I give an introduction to, and overview of, the use of GPUs in astronomy to date, highlighting the adoption and application trends from the first ~100 GPU-related publications in astronomy. I discuss the opportunities and challenges of utilising GPU computing clusters, such as the new Australian GPU supercomputer, gSTAR, for accelerating the rate of astronomical discovery.Comment: To appear in the proceedings of ADASS XXI, ed. P.Ballester and D.Egret, ASP Conf. Se

Scalability of Incompressible Flow Computations on Multi-GPU Clusters Using Dual-Level and Tri-Level Parallelism

High performance computing using graphics processing units (GPUs) is gaining popularity in the scientific computing field, with many large compute clusters being augmented with multiple GPUs in each node. We investigate hybrid tri-level (MPI-OpenMP-CUDA) parallel implementations to explore the efficiency and scalability of incompressible flow computations on GPU clusters up to 128 GPUS. This work details some of the unique issues faced when merging fine-grain parallelism on the GPU using CUDA with coarse-grain parallelism using OpenMP for intra-node and MPI for inter-node communication. Comparisons between the tri-level MPI-OpenMP-CUDA and dual-level MPI-CUDA implementations are shown using computationally large computational fluid dynamics (CFD) simulations. Our results demonstrate that a tri-level parallel implementation does not provide a significant advantage in performance over the dual-level implementation, however further research is needed to justify our conclusion for a cluster with a high GPU per node density or when using software that can utilize OpenMP’s fine-grain parallelism more effectively

Improving the User Experience of the rCUDA Remote GPU Virtualization Framework

Graphics processing units (GPUs) are being increasingly embraced by the high-performance computing community as an effective way to reduce execution time by accelerating parts of their applications. remote CUDA (rCUDA) was recently introduced as a software solution to address the high acquisition costs and energy consumption of GPUs that constrain further adoption of this technology. Specifically, rCUDA is a middleware that allows a reduced number of GPUs to be transparently shared among the nodes in a cluster. Although the initial prototype versions of rCUDA demonstrated its functionality, they also revealed concerns with respect to usability, performance, and support for new CUDA features. In response, in this paper, we present a new rCUDA version that (1) improves usability by including a new component that allows an automatic transformation of any CUDA source code so that it conforms to the needs of the rCUDA framework, (2) consistently features low overhead when using remote GPUs thanks to an improved new communication architecture, and (3) supports multithreaded applications and CUDA libraries. As a result, for any CUDA-compatible program, rCUDA now allows the use of remote GPUs within a cluster with low overhead, so that a single application running in one node can use all GPUs available across the cluster, thereby extending the single-node capability of CUDA. Copyright © 2014 John Wiley & Sons, Ltd.This work was funded by the Generalitat Valenciana under Grant PROMETEOII/2013/009 of the PROMETEO program phase II. The author from Argonne National Laboratory was supported by the US Department of Energy, Office of Science, under Contract No. DE-AC02-06CH11357. The authors are also grateful for the generous support provided by Mellanox Technologies.Reaño González, C.; Silla Jiménez, F.; Castello Gimeno, A.; Peña Monferrer, AJ.; Mayo Gual, R.; Quintana Ortí, ES.; Duato Marín, JF. (2015). Improving the User Experience of the rCUDA Remote GPU Virtualization Framework. Concurrency and Computation: Practice and Experience. 27(14):3746-3770. https://doi.org/10.1002/cpe.3409S374637702714NVIDIA NVIDIA industry cases http://www.nvidia.es/object/tesla-case-studiesFigueiredo, R., Dinda, P. A., & Fortes, J. (2005). Guest Editors’ Introduction: Resource Virtualization Renaissance. Computer, 38(5), 28-31. doi:10.1109/mc.2005.159Duato J Igual FD Mayo R Peña AJ Quintana-Ortí ES Silla F An efficient implementation of GPU virtualization in high performance clusters Euro-Par 2009 Workshops, ser. LNCS, 6043 Delft, Netherlands, 385 394Duato J Peña AJ Silla F Mayo R Quintana-Ortí ES Performance of CUDA virtualized remote GPUs in high performance clusters International Conference on Parallel Processing, Taipei, Taiwan 2011 365 374Duato J Peña AJ Silla F Fernández JC Mayo R Quintana-Ortí ES Enabling CUDA acceleration within virtual machines using rCUDA International Conference on High Performance Computing, Bangalore, India 2011 1 10Shi, L., Chen, H., Sun, J., & Li, K. (2012). vCUDA: GPU-Accelerated High-Performance Computing in Virtual Machines. IEEE Transactions on Computers, 61(6), 804-816. doi:10.1109/tc.2011.112Gupta V Gavrilovska A Schwan K Kharche H Tolia N Talwar V Ranganathan P GViM: GPU-accelerated virtual machines 3rd Workshop on System-Level Virtualization for High Performance Computing, Nuremberg, Germany 2009 17 24Giunta G Montella R Agrillo G Coviello G A GPGPU transparent virtualization component for high performance computing clouds Euro-Par 2010 - Parallel Processing, 6271 Ischia, Italy, 379 391Zillians VGPU http://www.zillians.com/vgpuLiang TY Chang YW GridCuda: a grid-enabled CUDA programming toolkit Proceedings of the 25th IEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA), Biopolis, Singapore 2011 141 146Barak A Ben-Nun T Levy E Shiloh A Apackage for OpenCL based heterogeneous computing on clusters with many GPU devices Workshop on Parallel Programming and Applications on Accelerator Clusters, Heraklion, Crete, Greece 2010 1 7Xiao S Balaji P Zhu Q Thakur R Coghlan S Lin H Wen G Hong J Feng W-C VOCL: an optimized environment for transparent virtualization of graphics processing units Proceedings of InPar, San Jose, California, USA 2012 1 12Kim J Seo S Lee J Nah J Jo G Lee J SnuCL: an OpenCL framework for heterogeneous CPU/GPU clusters Proceedings of the 26th International Conference on Supercomputing, Venice, Italy 2012 341 352NVIDIA The NVIDIA CUDA Compiler Driver NVCC Version 5, NVIDIA 2012Quinlan D Panas T Liao C ROSE http://rosecompiler.org/Free Software Foundation, Inc. GCC, the GNU Compiler Collection http://gcc.gnu.org/LLVM Clang: a C language family frontend for LLVM http://clang.llvm.org/Martinez G Feng W Gardner M CU2CL: a CUDA-to-OpenCL Translator for Multi- and Many-core Architectures http://eprints.cs.vt.edu/archive/00001161/01/CU2CL.pdfLLVM The LLVM compiler infrastructure http://llvm.org/Reaño C Peña AJ Silla F Duato J Mayo R Quintana-Orti ES CU2rCU: towards the complete rCUDA remote GPU virtualization and sharing solution Proceedings of the 19th International Conference on High Performance Computing (HiPC), Pune, India 2012 1 10NVIDIA The NVIDIA GPU Computing SDK Version 4, NVIDIA 2011Sandia National Labs LAMMPS molecular dynamics simulator http://lammps.sandia.gov/Citrix Systems, Inc. Xen http://xen.org/Peña AJ Virtualization of accelerators in high performance clusters Ph.D. Thesis, 2013NVIDIA CUDA profiler user's guide version 5, NVIDIA 2012Igual, F. D., Chan, E., Quintana-Ortí, E. S., Quintana-Ortí, G., van de Geijn, R. A., & Van Zee, F. G. (2012). The FLAME approach: From dense linear algebra algorithms to high-performance multi-accelerator implementations. Journal of Parallel and Distributed Computing, 72(9), 1134-1143. doi:10.1016/j.jpdc.2011.10.014Slurm workload manager http://slurm.schedmd.co

General‐purpose computation on GPUs for high performance cloud computing

This is the peer reviewed version of the following article: Expósito, R. R., Taboada, G. L., Ramos, S., Touriño, J., & Doallo, R. (2013). General‐purpose computation on GPUs for high performance cloud computing. Concurrency and Computation: Practice and Experience, 25(12), 1628-1642., which has been published in final form at https://doi.org/10.1002/cpe.2845. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.[Abstract] Cloud computing is offering new approaches for High Performance Computing (HPC) as it provides dynamically scalable resources as a service over the Internet. In addition, General‐Purpose computation on Graphical Processing Units (GPGPU) has gained much attention from scientific computing in multiple domains, thus becoming an important programming model in HPC. Compute Unified Device Architecture (CUDA) has been established as a popular programming model for GPGPUs, removing the need for using the graphics APIs for computing applications. Open Computing Language (OpenCL) is an emerging alternative not only for GPGPU but also for any parallel architecture. GPU clusters, usually programmed with a hybrid parallel paradigm mixing Message Passing Interface (MPI) with CUDA/OpenCL, are currently gaining high popularity. Therefore, cloud providers are deploying clusters with multiple GPUs per node and high‐speed network interconnects in order to make them a feasible option for HPC as a Service (HPCaaS). This paper evaluates GPGPU for high performance cloud computing on a public cloud computing infrastructure, Amazon EC2 Cluster GPU Instances (CGI), equipped with NVIDIA Tesla GPUs and a 10 Gigabit Ethernet network. The analysis of the results, obtained using up to 64 GPUs and 256‐processor cores, has shown that GPGPU is a viable option for high performance cloud computing despite the significant impact that virtualized environments still have on network overhead, which still hampers the adoption of GPGPU communication‐intensive applications. CopyrightMinisterio de Ciencia e Innovación; TIN2010-1673