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Histogram is a popular analytic graphical representation of data distribution resulting from processing a given numerical input data. Although the sequential histogram computation may be simple, it is no longer suitable in processing high volume of data. With recent advancement of high performance computing (HPC), aided by the accelerating growth of General Purpose Graphic Processing Unit (GPGPU),...
Recently, redundant network traffic elimination has attracted a lot of attention from both the academia and the industry. A core challenge and enabling technique in implementing redundancy elimination is to perform content-based chunking, which typically involves the computationally heavy Rabin fingerprinting algorithm. In this paper, we propose a GPU-based implementation of Rabin fingerprinting to...
GPUs can enable significant performance improvements for certain classes of data parallel applications and are widely used in recent computer systems. However, GPU execution currently requires explicit low-level operations such as 1) managing memory allocations and transfers between the host system and the GPU, 2) writing GPU kernels in a low-level programming model such as CUDA or OpenCL, and 3)...
We implement a promising algorithm for sparse-matrix sparse-vector multiplication (SpMSpV) on the GPU. An efficient k-way merge lies at the heart of finding a fast parallel SpMSpV algorithm. We examine the scalability of three approaches -- no sorting, merge sorting, and radix sorting -- in solving this problem. For breadth-first search (BFS), we achieve a 1.26x speedup over state-of-the-art sparse-matrix...
Graphics Processing Units (GPU) have been used extensively for accelerating parallelizable applications in general, and scientific computations in particular. Stencil based algorithms are used intensively in various research areas and represent good candidates for GPU based acceleration. Since scientific computations have high accuracy requirements, herein we focus on stencil based double precision...
Graphics processing units(GPUs) provide a low cost platform for accelerating high performance computations. New programming languages, such as CUDA and OpenCL, make GPU programming attractive to programmers. However, programming GPUs is still a cumbersome task for two reasons, tedious performance optimizations and lack of portability. First, optimizing an algorithm for a specific GPU is a time-consuming...
We present a high performance graphics processing unit (GPU) sorting algorithm ISSD (Improved Sorting considering Special Distributions) implemented with the Compute Unified Device Architecture (CUDA). The ISSD focuses on two aspects to improve parallel sorting performance. One is how to decompose the sorting tasks into independent and balanced subtasks which can then be easily distributed to thousands...
Graphics processing units (GPU) are considered to have superior performance over the central processing units (CPU) in performing common scientific computations. Number of factors that can seriously change this conception are usually overlooked. In this paper, some of these factors are taken into account and their impact is measured, analysed and discussed. Matrix multiplication and Shell sorting...
The enormous computational power available in modern graphics processing units (GPUs) has enabled the widely use of them for general-purpose applications. However, manual development of high-performance parallel codes for GPUs is still very challenging. In order to fully exploit the capability of GPU for general purpose computing under heterogeneous processing platforms, we propose performance estimation...
In this paper, we propose a source-to-source code optimization framework for general purpose computing on graphics processing units (GPGPU). Our framework is based on a re-formulation of the polyhedral loop transformation theory under the context of GPGPU. We prove that the number of actual memory transactions can be used as a performance metric to guide the code optimization process. In addition,...
The enormous computational power available in modern graphics processing units (GPUs) has enabled the widely use of them for general-purpose applications. However, manual development of high-performance parallel codes for GPUs is still very challenging. In order for improving GPGPU application performance by efficiently using GPU global memory, we extend the polyhedral model to capture memory access...
Graphs are a fundamental data representation that has been used extensively in various domains. In graph-based applications, a systematic exploration of the graph such as a breadth-first search (BFS) often serves as a key component in the processing of their massive data sets. In this paper, we present a new method for implementing the parallel BFS algorithm on multi-core CPUs which exploits a fundamental...
In this study, we test and analyze the performance of Gyrokinetic Torodial Code(GTC) program. According to the analysis results, we port GTC's compute-intensive subroutines to GPU and speed up them on the “CPU+GPU” heterogeneous architecture of TH-1A supercomputer. Some optimization strategies are developed in this process, for example, subroutines are integrated to reduce the data transfer between...
CUDA facilitates the development of General Purpose computing on Graphics Processing Units (GPGPU), however, its complex memory system, thread-level structure, and data transmission control between memories have brought great challenges for programming on GPU. In order to facilitate the development of parallel programs on GPU and reuse existing sequential codes, in this paper we propose a novel directive...
Option pricing is an important problem in computational finance due to the fast-growing market and increasing complexity of options. For option pricing, a model is required to describe the price process of the underlying asset. The GARCH model is one of the prominent option pricing models since it can model stochastic volatility of the underlying asset. To derive expected profit based on the GARCH...
The Graphics Processing Unit (GPU) is an asymmetric, heterogeneous multi-core architecture that can be used for high performance parallel computing applications. However, a significant level of interest has been focused on algorithms for solving regular problems, as these applications typically map well to the GPU. Irregular applications, which rely on pointer or graph-based data structures, have...
The speed of the memory subsystem often constrains the performance of large-scale parallel applications. Experts tune such applications to use hierarchical memory subsystems efficiently. Hardware accelerators, such as GPUs, can potentially improve memory performance beyond the capabilities of traditional hierarchical systems. However, the addition of such specialized hardware complicates code porting...
Genetic Algorithms(GAs) are suitable for parallel computing since population members fitness maybe evaluated in parallel. Most past parallel GA studies have exploited this aspect, besides resorting to different algorithms, such as island, single-population master-slave, fine-grained and hybrid models. A GA involves a number of other operations which, if parallelized, may lead to better parallel GA...
Solving complex convection-diffusion equations is very important to many practical mathematical and physical problems. After the finite difference discretization, most of the time for equations solution is spent on sparse linear equation solvers. In this paper, our goal is to solve 2D Nonlinear Unsteady Convection-Diffusion Equations by accelerating an iterative algorithm named Jacobi-preconditioned...
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