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High-Performance Graphics in Racket with DirectX 17th

High-Performance Graphics in Racket with DirectX

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17th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Bossard, Antoine Kanagawa Univ Grad Sch Sci 2946 Tsuchiya Hiratsuka Kanagawa 2591293 Japan

ISBN: (纸本)9783319654829;9783319654812

Nowadays, modern computer systems rely heavily on parallel processing, and not only because of the multicore CPUs bundled with any machine, even mobile devices, but more and more thanks to the parallel processing capacities of graphics processing units (GPU), general-purpose computing on graphics processing units (GPGPU) being one example. In this paper, relying on the DirectX 12 framework, we propose an innovative approach to enable parallel processing for graphical rendering on both the CPU and GPU for the popular Racket functional programming language (formerly PLT Scheme), and importantly without compromising Racket's usability and programmer-friendliness. Our performance evaluations show significant improvements with respect to execution time (x3 speed-up in some cases), CPU utilisation time (reduced by as much as 80% in some scenarios) and the frame rate when using moving graphics.

关键词： GPU parallel processing Functional Programming Scheme

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Exploration of Efficient Many-Core Processor for Dual-Polarization and Sympathetic Plucked String Model

Exploration of Efficient Many-Core Processor for Dual-Polari...

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5th international conference on Information Science and Applications (ICISA)

作者： Kang, Myeongsu Kim, Jong-Myon Univ Ulsan Dept Elect Engn Ulsan 680749 South Korea

ISBN: (纸本)9781479944415

Despite the fact that physics-based sound synthesis is becoming more and more efficient in rich and natural high-quality sound synthesis, its high computational complexity limits its use in portable devices. this constraint motivated research on parallel processing architectures that support the physics-based sound synthesis of musical instruments. Since no general consensus has been reached which grain sizes of many-core processors and memories provide the most efficient operation for sound synthesis, this paper explores a many-core processor for varying its PE configurations. To find the optimal PE configuration, each PE configuration is evaluated in terms of execution time, system power, and area. Experimental results indicate that the most efficient operation in order to synthesize 44,100 six-note polyphonic acoustic guitar sound sampled at 44.1 kHz is achieved as the number of PEs equals to 192. Likewise, all PE configurations used in this study are satisfied with the system requirements to implement sound synthesis on a portable device.

关键词： Design space exploration Many-core processor parallel processing architecture Physics-based sound synthesis

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A real-time ellipse detection method using GPU-based RANSAC

A real-time ellipse detection method using GPU-based RANSAC

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2013 5th international conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013

作者： Liu, Liu Yang, Jianfeng University of Chinese Academy of Sciences Beijing China Key laboratory of spectrum imaging technology Xi'an Institute of Optics and Fine Mechanics CAS Xi'an China Xi'an Institute of Optics and Fine Mechanics Chinese Academy of Sciences Xi'an China

ISBN: (纸本)9780769550114

An approach to very rapid and real-time ellipse detection is described, Based on a modified RANSAC (RANdom Sample Consensus), and considering the parallel processing capabilities of a programmable graphics processing unit. the method is able to detect ellipses of images in real-time. First, it finished the computational part of image segmentation and contour detection on the CPU. then it uses the fragment processing capability of the GPU to fit ellipse shape parameters. Experimental results show that the computational efficiency of the proposed method will be multiplied several times than other algorithms, and this method is more efficient for real-time ellipse detection. © 2013 IEEE.

关键词： Graphics processing unit

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Generic parallel Genetic Algorithm Framework for Protein Optimisation

Generic Parallel Genetic Algorithm Framework for Protein Opt...

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11th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Folkman, Lukas Pullan, Wayne Stantic, Bela Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld 4111 Australia

ISBN: (纸本)9783642246685

Proteins are one of the most vital macromolecules on the cellular level. In order to understand the function of a protein, its structure needs to be determined. For this purpose, different computational approaches have been introduced. Genetic algorithms can be used to search the vast space of all possible conformations of a protein in order to find its native structure. A framework for design of such algorithms that is both generic, easy to use and performs fast on distributed systems may help further development of genetic algorithm based approaches. We propose such a framework based on a parallel master-slave model which is implemented in C++ and Message Passing interface. We evaluated its performance on distributed systems with a different number of processors and achieved a linear acceleration in proportion to the number of processing units.

关键词： parallel Genetic Algorithm Protein Optimisation Protein Structure Prediction

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Proceedings of ScalA 2014: 5th Workshop on Latest Advances in Scalable algorithms for Large-Scale Systems - held in conjunction with SC 2014: the international conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of ScalA 2014: 5th Workshop on Latest Advances i...

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5th Workshop on Latest Advances in Scalable algorithms for Large-Scale Systems, ScalA 2014

ISBN: (纸本)9781479975624

the proceedings contain 10 papers. the topics discussed include: scaling parallel 3-D FFT with non-blocking MPI collectives;exploiting data representation for fault tolerance;VCube: a provably scalable distributed diagnosis algorithm;TX: algorithmic energy saving for distributed dense matrix factorizations;CholeskyQR2: a simple and communication-avoiding algorithm for computing a tall-skinny qr factorization on a large-scale parallel system;deflation strategies to improve the convergence of communication-avoiding GMRES;a framework for parallel genetic algorithms for distributed memory architectures;the anatomy of Mr. Scan: a dissection of performance of an extreme scale GPU-based clustering algorithm;performance and portability with OpenCL for throughput-oriented HPC workloads across accelerators, coprocessors, and multicore processors;and a hierarchical tridiagonal system solver for heterogenous supercomputers.

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GrABFAST: A CUDA based GPU Accelerated Fast Short Sequence Alignment Algorithm

GrABFAST: A CUDA based GPU Accelerated Fast Short Sequence A...

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19th international conference on High Performance Computing (HiPC)

作者： Narang, Ankur Soman, Jyothish Lahabar, Sheetal IBM India Res Labs Vasant Kunj Delhi 110070 India

ISBN: (纸本)9781467323703;9781467323727

Next Generation Sequencing (NGS) platforms typically produce short reads of size 50-150 base pairs (bp). the number of such short reads can be up to 6 billion per run. To align these short reads to a large genome is a computationally challenging problem. In this paper, we address this problem by considering the design and optimization of parallel sequence alignment on GPU based hybrid architectures. Even though the sequence alignment algorithm is inherently data-parallel, issues such as (a) space-time trade-offs in the Indexing schema, (b) need for fast candidate location search (CAL) on GPU, (c) maintaining low divergence along with low space for the dynamic programming based local alignment, make this a very challenging problem. We present the design of our novel parallel algorithm Graphics processor Accelerated BFAST (GrABFAST) for large scale read alignment that overcomes these challenges and demonstrates superior performance compared to Intel multicore architectures. Using 5 large genomes including those of Humans, Maize, Horse, Dog and Bacteria, we demonstrate a speedup of around 6x using Fermi Tesla C2070 GPUs vs the BFAST algorithm on 16 core Intel Xeon 5570 architecture.

关键词： dynamic programming graphics processing units multiprocessing systems parallel algorithms parallel architectures

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Performance Modeling of Multishift QR algorithms for the parallel Solution of Symmetric Tridiagonal Eigenvalue Problems

Performance Modeling of Multishift QR Algorithms for the Par...

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10th international conference on algorithms and architectures for parallel processing

作者： Miyata, Takafumi Yamamoto, Yusaku Zhang, Shao-Liang Nagoya Univ Aichi 4648603 Japan Kobe Univ Kobe Hyogo Japan

ISBN: (纸本)9783642131356

Multishift QR, algorithms are efficient for solving the symmetric tridiagonal eigenvalue problem on a parallel computer. In this paper, we focus on three variants of the multishift QR. algorithm, namely, the conventional multishift QR algorithm, the deferred shift QR, algorithm and the fully pipelined multishift QR, algorithm, and construct performance models for them. Our models are designed for shared-memory parallel machines, and given the basic performance characteristics of the target;machine and the problem size, predict the execution time of these algorithms. Experimental results show that our models can predict the relative performance of these algorithms to the accuracy of 10% in many cases. thus our models are useful for choosing the best algorithm to solve a given problem in a specified computational enviromnent, as well as for finding the best value of the performance parameters.

关键词： Eigenvalues and eigenfunctions

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Fast and parallel Computation of the Discrete Periodic Radon Transform on GPUs, multi-core CPUs and FPGAs 25

Fast and Parallel Computation of the Discrete Periodic Radon...

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25th IEEE international conference on Image processing (ICIP)

作者： Carranza, Cesar Pattichis, Marios Llamocca, Daniel Pontificia Univ Catolica Peru Secc Elect & Elect Lima 32 Peru Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Oakland Univ Dept Elect & Comp Engn Rochester MI 48063 USA

ISBN: (纸本)9781479970612

the Discrete Periodic Radon Transform (DPRT) has many important applications in reconstructing images from their projections and has recently been used in fast and scalable architectures for computing 2D convolutions. Unfortunately, the direct computation of the DPRT involves O(N-3) additions and memory accesses that can be very costly in single-core architectures. the current paper presents new and efficient algorithms for computing the DPRT and its inverse on multi-core CPUs and GPUs. the results are compared against specialized hardware implementations (FPGAs/ASICs). the results provide significant evidence of the success of the new algorithms. On an 8-core CPU (Intel Xeon), with support for two threads per core, FastDirDPRT and FastDirInvDPRT achieve a speedup of approximately 10x (up to 12:83x) over the single-core CPU implementation. On a 2048-core GPU (GTX 980), FastRayDPRT and FastRayInvDPRT achieve speedups in the range of 526 (for 127 x 127) to 873 (for 1021 x 1021), which approximate ideal speedups of what can be achieved. the DPRT can be computed exactly and in real-time (30 frames per second) for 1471x1471 images using FastRayDPRT on the GPU. Furthermore, the GPU algorithms approximate the performance of an efficient FPGA implementation using 2N parallel cores at 100MHz.

关键词： Discrete Periodic Radon Transform parallel Architecture Multi-core CPU GPU FPGA

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An Algorithmic Approach to Communication Reduction in parallel Graph algorithms 24

An Algorithmic Approach to Communication Reduction in Parall...

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24th international conference on parallel Architecture and Compilation (PACT)

作者： Harshvardhan Fidel, Adam Amato, Nancy M. Rauchwerger, Lawrence Texas A&M Univ Dept Comp Sci & Engn Parasol Lab College Stn TX 77843 USA

ISBN: (纸本)9781467395243

Graph algorithms on distributed-memory systems typically perform heavy communication, often limiting their scalability and performance. this work presents an approach to transparently (without programmer intervention) allow fine-grained graph algorithms to utilize algorithmic communication reduction optimizations. In many graph algorithms, the same information is communicated by a vertex to its neighbors, which we coin algorithmic redundancy. Our approach exploits algorithmic redundancy to reduce communication between vertices located on different processing elements. We employ algorithmaware coarsening of messages sent during vertex visitation, reducing both the number of messages and the absolute amount of communication in the system. To achieve this, the system structure is represented by a hierarchical graph, facilitating communication optimizations that can take into consideration the machine's memory hierarchy. We also present an optimization for small-world scale-free graphs wherein hub vertices (i.e., vertices of very large degree) are represented in a similar hierarchical manner, which is exploited to increase parallelism and reduce communication. Finally, we present a framework that transparently allows fine-grained graph algorithms to utilize our hierarchical approach without programmer intervention, while improving scalability and performance. Experimental results of our proposed approach on 131, 000+ cores show improvements of up to a factor of 8 times over the non-hierarchical version for various graph mining and graph analytics algorithms.

关键词： parallel graph processing graph analytics big data

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A parallel implementation of an XDraw viewshed algorithm with spark 21

A parallel implementation of an XDraw viewshed algorithm wit...

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21st IEEE international conference on High Performance Computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Jianbo, Zhang Caikun, Chen Tingnan, Liang Hao, Xia Simin, Zhou Faculty of Information Engineering China University of Geosciences Wuhan China

ISBN: (纸本)9781728120584

Viewshed analysis is an indispensable part of digital terrain analysis and is widely used in many application domains. High-resolution raster DEM data bring significant computational challenges to the existing viewshed analysis algorithms, which are computationally intensive and require a large memory space and massive computing power. the viewshed analysis process can be accelerated through the use of Apache Spark. In this article, we present both a tile-based raster data storing strategy and an equal-volume computing strategy for distributed viewshed computation using Spark. the parallel implementation of the XDraw algorithm mainly consists of three parts: (1) partitioning a raster DEM file into square tile sets and reorganizing these tile sets to prevent tile overlap across data divisions of HDFS, (2) subdividing the DEM into multiple equal-volume data sectors according to the viewpoint position, and (3) retrieving the corresponding tile sets of each sector to perform the XDraw algorithm independently and efficiently. Experiments with realworld datasets show that the two proposed strategies can achieve higher speed-up and efficiency for XDraw viewshed analysis as the raster DEM data volume is dramatically increased. © 2019 IEEE.

关键词： Sparks Partitioning algorithms Distributed databases Graphics processing units Geology parallel processing Approximation algorithms

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