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Graphics Processing Units (GPUs) have been successfully used to accelerate scientific applications due to their computation power and the availability of programming languages that make more approachable writing scientific applications for GPUs. However, since the programming model of GPUs requires offloading all the data to the GPU memory, the memory footprint of the application is limited to the...
We implemented and evaluated the triple precision Basic Linear Algebra Subprograms (BLAS) subroutines, AXPY, GEMV and GEMM on a Tesla C2050. In this paper, we present a Double Single (D+S) type triple precision floating-point value format and operations. They are based on techniques similar to Double-Double (DD) type quadruple precision operations. On the GPU, the D+S-type operations are more costly...
Design of data structures for high performance computing (HPC) is one of the principal challenges facing researchers looking to utilize heterogeneous computing machinery. Heterogeneous systems derive cost, power, and speed efficiency by being composed of the appropriate hardware for the task. Yet, each type of processor requires a specific organization of the application state in order to achieve...
Computational fluid dynamics (CFD) applications have an ever-growing demand for the power of high performance computing (HPC) infrastructure. Many CFD simulations have benefited from newly-acknowledged GPU clusters. However, few of them have exploited both the CPU and the GPU computational resources within the heterogeneous HPC platforms. In this paper, we endeavor to demonstrate the approach of making...
Characteristics of full applications found in scientific computing industries today lead to challenges that are not addressed by state-of-the-art approaches to automatic parallelization.These characteristics are not present in CPU kernel codes nor linear algebra libraries, requiring a fresh look at how to make automatic parallelization apply to today's computational industries using full applications...
We analyze and compare several data structures and algorithms for evaluating the kernel density estimate. Frequent evaluations of this estimate are for example needed for plotting, error estimation, Monte Carlo estimation of probabilities and functionals, and pattern classification. An experimental comparison is included.
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