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2 2021

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Performance enhancement of CUDA applications by overlapping data transfer and Kernel execution

Raju K., Chiplunkar Niranjan N

Applied Computer Science

2021

Vol. 17, no 3

5--18

The CPU-GPU combination is a widely used heterogeneous computing system in which the CPU and GPU have different address spaces. Since the GPU cannot directly access the CPU memory, prior to invoking the GPU function the input data must be available on the GPU memory. On completion of GPU function, the results of computation are transferred to CPU memory. The CPU-GPU data transfer happens through PCIExpress bus. The PCI-E bandwidth is much lesser than that of GPU memory. The speed at which the data is transferred is limited by the PCI-E bandwidth. Hence, the PCI-E acts as a performance bottleneck. In this paper two approaches are discussed to minimize the overhead of data transfer, namely, performing the data transfer while the GPU function is being executed and reducing the amount of data to be transferred to GPU. The effectiveness of these approaches on the execution time of a set of CUDA applications is realized using CUDA streams. The results of our experiments show that the execution time of applications can be minimized with the proposed approaches.

Machine Learning and High-Performance Computing Hybrid Systems, a New Way of Performance Acceleration in Engineering and Scientific Applications

Gepner Pawel

Annals of Computer Science and Information Systems

2021

Vol. 25

27--36

Machine learning is one of the hottest topics in IT industry as well as in academia. Some of the IT leaders and scientists believe that this is going to totally revolutionise the industry. This transformation is happening on both fronts, one is the application and software paradigm, the other is at the hardware and system level. At the same time, the High-Performance Computing segment is striving to achieve the level of Exascale performance. It is not debatable that to meet such level of performance and keep the cost of system and power consumption on reasonable level is not a trivial task. In this article, we try to look at a potential solution to these problems and discuss a new approach to building systems and software to meet these challenges and the growing needs of the computing power for HPC systems on the one hand, but also be ready for a new type of workload including Artificial Intelligence type of applications.