Design of a Resource Management for GPGPU Supported Grid Computing

Year 2016, Volume: 1 Issue: 1, 39 - 49, 01.12.2016

Emrah Dönmez

Abstract

—
In this study; we aimed to propose design of a QoS aware resource management
infrastructure for a GPGPU supported Grid computing system. This Grid system
consists of hybrid (CPU + CPU) and heterogeneous (Nvidia + AMD Radeon) GPGPU
computational nodes. It can manage both small scale unit (connections, threads,
buffer pools etc.) and large scale unit (whole computing machines). As
increasing of the network communication bandwidth and developing powerful
computer hardware (CPU, GPU etc.), distributed computing systems acquire more
and more attention day by day. Grid computing is as a major player in such kind
of distributed system environments like cloud, volunteer, hybrid and etc. Since
it supports large scale resource sharing between geographically distributed
computer clusters and even single computers. Nowadays, there is another
important technology pillar to implement high performance computing rather than
CPU, it is known as GPU computing. The GPU systems are ideal especially to data
intensive applications; such as image processing, data mining, financial
computations etc. Therefore, GPU based grids give an undertaking higher
computational performance. GPU processor consists of lots of controllable cores
which can be used for high performance demanded applications. Ultimately, the
major concerns in grid computing are particularly related to managing QoS
requirements, granularity of resources, and heterogeneous resources (both CPU
and GPU).  

Keywords

Grid computing, GPGPU, QoS, resource management

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