16,332 research outputs found
A Survey of Techniques for Improving Security of GPUs
Graphics processing unit (GPU), although a powerful performance-booster, also
has many security vulnerabilities. Due to these, the GPU can act as a
safe-haven for stealthy malware and the weakest `link' in the security `chain'.
In this paper, we present a survey of techniques for analyzing and improving
GPU security. We classify the works on key attributes to highlight their
similarities and differences. More than informing users and researchers about
GPU security techniques, this survey aims to increase their awareness about GPU
security vulnerabilities and potential countermeasures
Architecture-Aware Optimization on a 1600-core Graphics Processor
The graphics processing unit (GPU) continues to
make significant strides as an accelerator in commodity cluster
computing for high-performance computing (HPC). For example,
three of the top five fastest supercomputers in the world, as
ranked by the TOP500, employ GPUs as accelerators. Despite this
increasing interest in GPUs, however, optimizing the performance
of a GPU-accelerated compute node requires deep technical
knowledge of the underlying architecture. Although significant
literature exists on how to optimize GPU performance on the
more mature NVIDIA CUDA architecture, the converse is true
for OpenCL on the AMD GPU.
Consequently, we present and evaluate architecture-aware optimizations
for the AMD GPU. The most prominent optimizations
include (i) explicit use of registers, (ii) use of vector types, (iii)
removal of branches, and (iv) use of image memory for global data.
We demonstrate the efficacy of our AMD GPU optimizations by
applying each optimization in isolation as well as in concert to
a large-scale, molecular modeling application called GEM. Via
these AMD-specific GPU optimizations, the AMD Radeon HD
5870 GPU delivers 65% better performance than with the wellknown
NVIDIA-specific optimizations
Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines
In this paper, we address the problem of efficient execution of a computation
pattern, referred to here as the irregular wavefront propagation pattern
(IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in
several image processing operations. In the IWPP, data elements in the
wavefront propagate waves to their neighboring elements on a grid if a
propagation condition is satisfied. Elements receiving the propagated waves
become part of the wavefront. This pattern results in irregular data accesses
and computations. We develop and evaluate strategies for efficient computation
and propagation of wavefronts using a multi-level queue structure. This queue
structure improves the utilization of fast memories in a GPU and reduces
synchronization overheads. We also develop a tile-based parallelization
strategy to support execution on multiple CPUs and GPUs. We evaluate our
approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs
and 2 multicore CPUs) using the IWPP implementations of two widely used image
processing operations: morphological reconstruction and euclidean distance
transform. Our results show significant performance improvements on GPUs. The
use of multiple CPUs and GPUs cooperatively attains speedups of 50x and 85x
with respect to single core CPU executions for morphological reconstruction and
euclidean distance transform, respectively.Comment: 37 pages, 16 figure
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