Yong-Yeon Jo
ABSTRACT
The graphic processing unit (GPU) is a computing resource to process graphics-related applications. The intelligent SSD (iSSD) is a solid state device (SSD) that is provided with data processing power. These days, CPU, GPU, and SSD are equipped together in most processing environment. If SSD is replaced with iSSD later on, we have a new processing environment where three computing resources collaborate one another to process a huge volume of data (so called big data) quite effectively. In this paper, we address how to exploit all these computing resources for efficient processing of data-intensive algorithms.Through extensive experiment, we verify the effectiveness and potential of the proposed collaborative processing environment by processing data concurrently with multiple computing resources. The results reveal that processing in the our environment outperforms that in the traditional one by up to 3.5 times.
- D. Bae et al., "Intelligent SSD: A Turbo for Big Data Mining," In Proc. of ACM Int'l Conf. on Information and Knowledge Management, ACM CIKM, pp. 1553--1556, 2013. Google Scholar
Digital Library
- N. Gov et al., "GPUTeraSort: High Performance Graphics Coprocessor Sorting for Large Database Management," In Proc. ACM Int'l. Conf. on Management of Data, ACM SIGMOD, pp. 325--336, 2006. Google ScholarDigital Library
- J. Fung and S. Mann, "Using Graphics Devices in Reverse: GPU-based Image Processing and Computer Vision," In Proc. IEEE Int'l Conf. Multimedia and Expo, pp. 9--12, 2008.Google Scholar
- S. Ryoo et al., "Optimization Principles and Application Performance Evaluation of a Multithreaded GPU using CUDA," In Proc. ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming, PPoPP, pp. 73--82, 2008 Google ScholarDigital Library
- S. Kim et al., "Fast, Energy Efficient Scan inside Flash Memory SSDs," In Proc. Int'l Workshop on Accelerating Data Management Systems using Modern Processor and Storage Architectures, ADMS, 2011.Google Scholar
- Y. Jo et al., "On Running Data-Intensive Algorithms with Intelligent SSD and Host CPU: A Collaborative Approach," In Proc. Int'l Conf. on ACM/SIGAPP Symposium On Applied Computing, ACM SAC, pp. 2060--2065, 2015. Google ScholarDigital Library
- S. Pabst, A. Koch, and W. Straber, "Fast and Scalable CPU=GPU Collision Detection for Rigid and Deformable Surfaces," Computer Graphics Forum, Vol. 29, No. 5, pp. 1605--1612, 2010.Google Scholar
- H. Oh and S. Ha, "A Static Scheduling Heuristic for Heterogeneous Processors," In Proc. Int'l Conf. Euro-Par Parallel Processing, pp. 573--577, 1996. Google ScholarDigital Library
- H. Topcuoglu, S. Hariri and M. Wu, "Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing," IEEE Transactions on Parallel and Distributed Systems, Vol. 3, No. 3, pp. 260--274. 2002. Google ScholarDigital Library
- G. Sih and E. Lee, "A Compile-Time Scheduling Heuristic for Interconnection-Constrained Heterogeneous Processor Architectures," IEEE Transactions on Parallel and Distributed Systems, Vol. 4, No. 2, pp. 175--187. 1993. Google ScholarDigital Library
- Y. Kwok and I. Ahmad, "Static Scheduling Algorithms for Allocating Directed Task Graphs to Multiprocessors," ACM Computing Surveys, Vol. 31, No. 4, 1999. Google ScholarDigital Library
- N. Bell and M. Garland, Efficient Sparse Matrix-Vector Multiplication on CUDA, NVIDIA Technical Report, NVIDIA Corporation, 2008.Google Scholar
- E. Lee and D. Messerschmitt, "Synchronous data flow," Proceedings of the IEEE, Vol. 75, No. 9, pp. 1235--1245, 1987.Google ScholarCross Ref
- J. MacQueen et al., "Some Methods for Classification and Analysis of Multivariate Observations," In Proc. of Berkeley Symp. on Mathematical Statistics and Probability, pp. 281--297, 1967.Google Scholar
- L. Page et al., The PageRank Citation Ranking: Bringing Order to the Web, Technical Report, Stanford University, 1999.Google Scholar
- G. Jeh and J. Widom, "SimRank: a measure of structural-context similarity," In Proc. of ACM Int'l. Conf. on Knowledge discovery and data mining, ACM SIGKDD, pp. 538--543, 2002. Google ScholarDigital Library
- Intel, Intel VTune Amplifier, https://software.intel.com/en-us/node/529213, 2014.Google Scholar
Index Terms
- Collaborative processing of data-intensive algorithms with CPU, intelligent SSD, and GPU
Recommendations
On running data-intensive algorithms with intelligent SSD and host CPU: a collaborative approach
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied ComputingA solid state device (SSD), which has the characteristics such as high IO bandwidth and low access latency, is drawing attention as a next-generation storage device. Even though SSD provides a high internal bandwidth, the performance bottleneck exists ...
Adaptive Optimization for Petascale Heterogeneous CPU/GPU Computing
CLUSTER '10: Proceedings of the 2010 IEEE International Conference on Cluster ComputingIn this paper, we describe our experiment developing an implementation of the Linpack benchmark for TianHe-1, a petascale CPU/GPU supercomputer system, the largest GPU-accelerated system ever attempted before. An adaptive optimization framework is ...
Heterogeneous concurrent execution of Monte Carlo photon transport on CPU, GPU and MIC
IA3 '14: Proceedings of the 4th Workshop on Irregular Applications: Architectures and AlgorithmsIn this paper, a new level of heterogeneous concurrent execution of Monte Carlo photon transport is presented. ARCHER, an application for computing radiation dosimetry for CT imaging involving whole-body patient phantoms has been extended to execute on ...
Comments