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OpenCL-Based Erasure Coding on Heterogeneous Architectures

Guoyang Chen, Huiyang Zhou, Xipeng Shen, Josh Gahm, Narayan Venkat, Skip Booth, John Marshall

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Tags: Algorithms, APU, ATI, Computer science, Error recovery, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K40

June 14, 2016 by hgpu

Processing Big Data in Main Memory and on GPU

Meisam Fathi Salmi

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Tags: big data, Computer science, CUDA, Hadoop, Java, OpenCL, Scala, Spark, Thesis

June 14, 2016 by hgpu

Multi-GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL

Jan Masek, Radim Burget, Lukas Povoda, Malay Kishore Dutta

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Tags: Algorithms, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 690, OpenCL

June 14, 2016 by hgpu

Analysis and Parameter Prediction of Compiler Transformation for Graphics Processors

Alberto Magni

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7970, Computer science, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 480, OpenCL, Package, performance portability, Tesla K20, Thesis

June 9, 2016 by hgpu

Decoupled Vector-Fetch Architecture with a Scalarizing Compiler

Yunsup Lee

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Tags: Computer science, CUDA, Electronic design automation, Heterogeneous systems, LLVM, OpenCL, Performance, PTX, SoC, Thesis

June 9, 2016 by hgpu

OpenMP Parallelization and Optimization of Graph-based Machine Learning Algorithms

Zhaoyi Meng, Alice Koniges, Yun (Helen) He, Samuel Williams, Thorsten Kurth, Brandon Cook, Jack Deslippe, Andrea L. Bertozzi

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Tags: Clustering, Computer science, Intel Xeon Phi, Machine learning, OpenMP

June 9, 2016 by hgpu

Adaptive Multi-level Blocking Optimization for Sparse Matrix Vector Multiplication on GPU

Yusuke Nagasaka, Akira Nukada, Satoshi Matsuoka

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla K20

June 9, 2016 by hgpu

Runtime Specialization for Heterogeneous CPU-GPU Platforms

Naila Farooqui

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Tags: ATI, ATI Radeon HD 7770, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, PTX, Tesla C2050, Tesla M2075, Thesis

June 9, 2016 by hgpu

Massively-Parallel Lossless Data Decompression

Evangelia Sitaridi, Rene Mueller, Tim Kaldewey, Guy Lohman, Kenneth Ross

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, Tesla K40

June 7, 2016 by hgpu

Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms

Matthew Moskewicz, Forrest Iandola, Kurt Keutzer

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Tags: ARM, CNN, Computer science, Deep learning, Linear Algebra, Machine learning, Mathematical Software, Neural networks, OpenCL

June 7, 2016 by hgpu

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

Jeffrey Kelling, Geza Odor, Sibylle Gemming

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Tags: Computational Physics, Computer science, CUDA, Materials Science, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce Titan Black, Statistical Mechanics, Tesla K80

June 7, 2016 by hgpu

Co-tuning of Software Specializers and Hardware Accelerators within a CNN Application

Sean Roberts

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Tags: ARM, CNN, Computer science, Deep learning, FPGA, Neural networks, SoC

June 7, 2016 by hgpu

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

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high performance computing on graphics processing units: hgpu.org

Applications

OpenCL-Based Erasure Coding on Heterogeneous Architectures

Processing Big Data in Main Memory and on GPU

Multi-GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL

Analysis and Parameter Prediction of Compiler Transformation for Graphics Processors

Decoupled Vector-Fetch Architecture with a Scalarizing Compiler

OpenMP Parallelization and Optimization of Graph-based Machine Learning Algorithms

Adaptive Multi-level Blocking Optimization for Sparse Matrix Vector Multiplication on GPU

Runtime Specialization for Heterogeneous CPU-GPU Platforms

Massively-Parallel Lossless Data Decompression

Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

Co-tuning of Software Specializers and Hardware Accelerators within a CNN Application

Recent source codes

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

HPX: a C++ Standard Library for Concurrency and Parallelism

TorchQC: Quantum Dynamics and Machine Learning

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

tdg-benchs: benchmarks used to test the performance of taskgraph

LLOR: Automatic Repair of OpenMP Programs

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