NDLI: VAST: the illusion of a large memory space for GPUs

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VAST: the illusion of a large memory space for GPUs

Content Provider	ACM Digital Library
Author	Samadi, Mehrzad Mahlke, Scott Lee, Janghaeng
Abstract	Heterogeneous systems equipped with traditional processors (CPUs) and graphics processing units (GPUs) have enabled processing large data sets. With new programming models, such as OpenCL and CUDA, programmers are encouraged to offload data parallel workloads to GPUs as much as possible in order to fully utilize the available resources. Unfortunately, offloading work is strictly limited by the size of the physical memory on a specific GPU. In this paper, we present Virtual Address Space for Throughput processors (VAST), an automatic GPU memory management system that provides an OpenCL program with the illusion of a virtual memory space. Based on the available physical memory on the target GPU, VAST does the following: automatically partitions the data parallel workload into chunks; efficiently extracts the precise working set required for the divided workload; rearranges the working set in contiguous memory space; and, transforms the kernel to operate on the reorganized working set. With VAST, the programmer is responsible for developing a data parallel kernel in OpenCL without concern for physical memory space limitations of individual GPUs. VAST transparently handles code generation dealing with the constraints of the actual physical memory and improves the retargetability of the OpenCL with moderate overhead. Experiments demonstrate that a real GPU, NVIDIA GTX 760 with 2 GB of memory, can compute any size of data without program changes achieving 2.6x speedup over CPU exeuction, which is a realistic alternative for large data computation.
Starting Page	443
Ending Page	454
Page Count	12
File Format	PDF
ISBN	9781450328098
DOI	10.1145/2628071.2628075
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Virtual memory Gpu Optimization
Content Type	Text
Resource Type	Article

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