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GPU-based Parallel Computation Support for Stan

Rok Cesnovar, Steve Bronder, Davor Sluga, Jure Demsar, Tadej Ciglaric, Sean Talts, Erik Strumbelj

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Tags: AMD Radeon R9 Fury, ATI, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Tesla V100

July 10, 2019 by hgpu

Massively Parallel GPU Memory Compaction

Matthias Springer, Hidehiko Masuhara

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan XP, Performance

May 30, 2019 by hgpu

Speeding-up the Verification Phase of Set Similarity Joins in the GPGPU paradigm

Christos Bellas, Anastasios Gounaris

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX Titan XP, Package

December 30, 2018 by hgpu

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Patrick Lavin, Jason Riedy, Rich Vuduc, Jeffrey Young

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Tags: Benchmarking, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Performance, Tesla P100

November 18, 2018 by hgpu

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

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Tags: Artificial intelligence, Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce GTX Titan XP, nVidia Tesla GP100

November 11, 2018 by hgpu

A Hybrid GPU-FPGA-based Computing Platform for Machine Learning

Xu Liu, Hibat Allah Ounifi, Abdelouahed Gherbi, Yves Lemieux, Wubin Li

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Tags: Algorithms, Benchmarking, CNN, Computer science, FPGA, Hybrid computing, Machine learning, nVidia, nVidia GeForce GTX Titan XP, OpenCL

November 11, 2018 by hgpu

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, OpenMPI, Package

August 11, 2018 by hgpu

Efficient Large-scale Approximate Nearest Neighbor Search on OpenCL FPGA

Jialiang Zhang, Soroosh Khoram, Jing Li

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Tags: Computer science, FPGA, Nearest neighbour, nVidia, nVidia GeForce GTX Titan XP, OpenCL

June 13, 2018 by hgpu

TBD: Benchmarking and Analyzing Deep Neural Network Training

Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, nVidia Quadro P 4000

March 22, 2018 by hgpu

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Xiaolong Xie, Yun Liang, Xiuhong Li, Wei Tan

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Tags: Algorithms, Computer science, CUDA, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Package, Tesla V100

March 17, 2018 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

GPU-based Parallel Computation Support for Stan

Massively Parallel GPU Memory Compaction

Speeding-up the Verification Phase of Set Similarity Joins in the GPGPU paradigm

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

A Hybrid GPU-FPGA-based Computing Platform for Machine Learning

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Efficient Large-scale Approximate Nearest Neighbor Search on OpenCL FPGA

TBD: Benchmarking and Analyzing Deep Neural Network Training

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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