|
Multi-GPU Volume Visualization using MapReduce
Jeff A. Stuart, Cheng-Kai Chen, Kwan-Liu Ma, and John D. Owens
Abstract
We present a novel MapReduce library, GPMR, that leverages the power of GPU-based
clusters for large-scale computing. To better utilize GPU resources, we
modify the MapReduce paradigm by combining large amounts of individual map and
reduce items into chunks, using partial reductions, and using accumulation. We
use persistent map and reduce tasks and stress individual aspects of GPMR by
porting a suite of MapReduce benchmarks (Word Occurrence, Sparse Integer
Occurrence, Linear Regression, Matrix Multiplication, and K-Means Clustering) to
GPMR. We execute these benchmarks using a cluster of sixty-four NVIDIA GPUs
among sixteen compute nodes and achieve desirable speedup and efficiency for all
benchmarks. We compare our implementation to the current best GPU MapReduce
library (which runs only on a single GPU) and a highly-optimized multi-core
implemention of MapReduce to show the power of optimized multi-GPU MapReduce. We
demonstrate how typical MapReduce applications are easily modified to fit into
GPMR and thus effectively leverage a cluster of GPUs. We highlight how the ratio
of computation between different phases of the pipeline, as well as the ratio
between communication and computation, affects results from GPMR. We conclude
with an exposition on the types of MapReduce tasks well-suited to GPMR, and why
some tasks require more modification than others to work well with the GPU.
|
