Current trends in heterogeneous systems: A review
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Abstract
Looking at the heterogeneous system, this paper touches on the architecture of heterogeneous systems, program models, and some challenges in this field. Heterogeneous systems have become an important development trend in the current high-performance computing field. Heterogeneous systems can be seen from smartwatches, mobile phones and laptops to server systems. This paper will explore different types of heterogeneous systems such as CPU-GPU architecture, and ARM big. Little architecture. The program model for different types of heterogeneous systems where we will see how different architecture heterogeneous systems work. Then we will see some of the challenges in the heterogeneous system and finally some recommendations for future work.
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Yang XJ, Liao XK, Lu K. The TianHe-1A supercomputer: its hardware and software. Journal of Computer Science and Technology. 2011; 26(3): 344-351.
Brodtkorb AR, Dyken C, Hagen TR, Hjelmervik JM, Storaasli OO. State-of-the-art in Heterogeneous Computing. Scientific Programming. 2010; 1-33.
Lyerly R, Antonio B, Christopher J, Vincent L, Anthony C, Binoy R. Operating System Process and Thread Migration in Heterogeneous Platforms. 2016.
Baumann A, Barham P, Dagand PE, Harris T, Isaacs R, Peter S, Roscoe T, Schupbach A, Singhania A. The Multikernel: A New OS Architecture for Scalable Multicore Systems. In Proceedings of the ACM SIGOPS 22Nd Symposium on Operating Systems Principles. SOSP. 2009; 9.
Beckmann N, Sanchez D. Jigsaw: Scalable Software-defined Caches. In Proceedings of the 22Nd International Conference on Parallel Architectures and Compilation Techniques, PACT. IEEE. 2013; 213–224.
Brodtkorb A, Hagen T, Sætra M. Graphics processing unit (GPU) programming strategies and trends in GPU computing. Journal of Parallel and Distributed Computing. 2013; 73: 4–13.
Nvidia Corporation. Compute unified device architecture programming guide [OL]. https://developer.nvidia.com/cuda-zone, 2022
Daga M, Aji AM, Feng WC. On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing. 2011 Symposium on Application Accelerators in High-Performance Computing. 2011: 141-149.
Liu X, Smelyanskiy M, Chow E, Dubey P. Efficient sparse matrix-vector multiplication on x86-based many-core processors. In Proceedings of the 27th international ACM conference on International conference on supercomputing (ICS '13). Association for Computing Machinery, New York, NY, USA, 2013; 273–282.
ARM Technologies. https://www.arm.com/technologies/big-little, 2022
Barbalace A, Sadini M, Ansary S, Jelesnianski C, Ravichandran A, Kendir C, Murray A, Ravindran B. Popcorn: Bridging the Programmability Gap in heterogeneous-ISA Platforms. In Proceedings of the Tenth European Conference on Computer Systems, EuroSys ’15. 2015; 29:1–29.
Gottipati S. 2021. Exploring ARM and heterogeneous compute architecture. https://www.druva.com/blog/exploring-arm-and-heterogeneous-compute-architecture/
Zhong Z, Rychkov V, Lastovetsky A. Data partitio-ning on heterogeneous multicore and multi-GPU systems using functional performance models of data-parallel applications. In: 2012 IEEE International Conference on Cluster Computing. 2012; 191-199.
Kalidas R, Daga M, Keommydas K. On the Per-formance,Energy,and Power of Data-Access Methods in Heter-ogeneous Computing Systems. In: IEEE International Parallel &Distributed Processing Symposium Workshop. IEEE. 2015.
Goddeke D, Wobker H, Strzodka R. Co-processor acceleration of an unmodified parallel solid mechanics codewith FEASTGPU. International Journal of Computational Science and Engineering. 2009; 4(4): 254-269.