Title page for ETD etd-08242012-153113


Type of Document Dissertation
Author Elteir, Marwa Khamis
Author's Email Address maelteir@vt.edu
URN etd-08242012-153113
Title A MapReduce Framework for Heterogeneous Computing Architectures
Degree PhD
Department Computer Science
Advisory Committee
Advisor Name Title
Wu-chun Feng Committee Chair
Heshan Lin Committee Co-Chair
Ali Butt Committee Member
Eli Tilevich Committee Member
Xiaosong Ma Committee Member
Keywords
  • Atomics
  • Graphics Processing Units
  • Programming Models
  • Heterogeneous Computing
  • MapReduce
Date of Defense 2012-08-15
Availability unrestricted
Abstract

Nowadays, an increasing number of computational systems are equipped with heterogeneous compute resources, i.e., following different architecture. This applies to the level of a single chip, a single node and even supercomputers and large-scale clusters. With its impressive price-to-performance ratio as well as power efficiently compared to traditional multicore processors, graphics processing units (GPUs) has become an integrated part of these systems. GPUs deliver high peak performance; however efficiently exploiting their computational power requires the exploration of a multi-dimensional space of optimization methodologies, which is challenging even for the well-trained expert. The complexity of this multi-dimensional space arises not only from the traditionally well known but arduous task of architecture-aware GPU optimization at design and compile time, but it also arises in the partitioning and scheduling of the computation across these heterogeneous resources. Even with programming models like the Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL), the developer still needs to manage the data transfer be- tween host and device and vice versa, orchestrate the execution of several kernels, and more arduously, optimize the kernel code.

In this dissertation, we aim to deliver a transparent parallel programming environment for heterogeneous resources by leveraging the power of the MapReduce programming model and OpenCL programming language. We propose a portable architecture-aware framework that efficiently runs an application across heterogeneous resources, specifically AMD GPUs and NVIDIA GPUs, while hiding complex architectural details from the developer. To further enhance performance portability, we explore approaches for asynchronously and efficiently distributing the computations across heterogeneous resources. When applied to benchmarks and representative applications, our proposed framework significantly enhances performance, including up to 58% improvement over traditional approaches to task assignment and up to a 45-fold improvement over state-of-the-art MapReduce implementations.

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