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This paper describes initial steps to leverage accelerators, such as GPUs, in ab initio nuclear physics calculations. Specifically, parallel nuclear structure calculations performed by the MFDn package are considered with selected stages adapted for GPUs. This paper outlines the necessary steps to make MFDnutilize GPUs in its matrix construction stage. The experiments are presented to compare the...
Modern GPUs support special protocols to exchange data directly across the PCI Express bus. While these protocols could be used to reduce GPU data transmission times, basically by avoiding staging to host memory, they require specific hardware features which are not available on current generation network adapters. In this paper we describe the architectural modifications required to implement peer-to-peer...
In this work, we present our implementation of a three-dimensional 5th order finite-difference weighted essentially non-oscillatory (WENO) scheme in double precision on CPU/GPU clusters, which targets on large-scale cosmological hydrodynamic flow simulations involving both shocks and complicated smooth solution structures. In the level of MPI parallelization, we subdivided the domain along each of...
The increasing power and decreasing cost of Graphic Processing Units (GPUs) together with the development of programming languages for General Purpose Computing on GPUs (GPGPU) have led to the development and implementation of fast parallel algorithms for this architecture for a large spectrum of applications. Given the streaming-processing characteristics of GPUs, most practical applications so far...
Highly optimized library implementations for important scientific kernels can improve scientific productivity. To this end, we are currently developing the Phylogenetic Likelihood Library (PLL) that implements functions to compute and optimize the phylogenetic likelihood score on evolutionary trees. Here, we focus on novel techniques to orchestrate likelihood computations on large vector-like processors...
The Discrete Memory Machine (DMM) and the Unified Memory Machine (UMM) are theoretical parallel computing models that capture the essence of the shared memory access and the global memory access of GPUs. The main contribution of this paper is to introduce the Hierarchical Memory Machine (HMM), which consists of multiple DMMs and a single UMM. The HMM is a more practical parallel computing model which...
Multiprocessing modular exponentiation has a variety of uses, including cryptography, prime testing and computational number theory. It is also a very costly operation to compute. GPU parallelism can be used to accelerate these computations, but to use the GPU efficiently, a problem must involve a significant number of simultaneous exponentiation operations. Handling a large number of TLS/SSL encrypted...
How can parallel computing topics be incorporated into core courses that are taken by the majority of undergraduate students? This paper reports our experiences adding GPU computing with CUDA into the core undergraduate computer organization course at two different colleges. We have found that even though programming in CUDA is not necessarily easy, programmer control and performance impact seem to...
The Deep Greedy Switching algorithm is a fast heuristic for solving large instances of the linear sum assignment problem whilst sacrificing very little in terms of optimality. In this paper we explore the worst case performance aspects of the algorithm. We prove that the algorithm is finite and analyze its computational complexity. We also discuss a number of simplified variations of the algorithm...
Pattern matching is an important operation in various applications such as computer and network security, bioinformatics, image processing, among many others. Aho-Corasick (AC) algorithm is a multiple patterns matching algorithm commonly used for such applications. In order to meet the highly demanding performance requirements imposed on these applications, achieving high performance for AC algorithm...
Many core architectural computing devices such as Graphic Processing Unit (GPU) are becoming increasingly popular in scientific computing because of their performance advantages. The scaling trends of processor and memory technologies demand more innovations in memory and processor architectures, e.g., the need for new architectural techniques to leverage Non-Volatile Random Access Memories (NVRAM)...
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