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A Qualitative Comparison Study Between Common GPGPU Frameworks

Adam Soderstrom

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Tags: Computer science, CUDA, DirectCompute, DirectX, N-body simulation, nVidia, nVidia GeForce GTX 1050, OpenCL, Thesis

August 26, 2018 by hgpu

Kernel Tuner: A search-optimizing GPU code auto-tuner

Ben van Werkhoven

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Tags: AMD Radeon Vega Frontier Edition, ATI, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, OpenCL, Optimization, Package

August 19, 2018 by hgpu

Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures

Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke

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Tags: Computer science, Deep learning, Intel Xeon Phi, OpenMP, Package, Performance

August 19, 2018 by hgpu

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

Wei Tan, Shiyu Chang, Liana Fong, Cheng Li, Zijun Wang, Liangliang Cao

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Tags: Algorithms, Compression, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Package

August 19, 2018 by hgpu

libhclooc: Software Library Facilitating Out-of-core Implementations of Accelerator Kernels on Hybrid Computing Platforms

Daniel Hanlon, Hamidreza Khalighzadeh, Ravi Reddy Manumachu, Alexey Lastovetsky

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Tags: Cloud, Computer science, CUDA, FPGA, Hybrid computing, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package

August 19, 2018 by hgpu

A Cross-platform Evaluation of Graphics Shader Compiler Optimization

Lewis Crawford, Michael O'Boyle

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Tags: 3D Graphics and Realism, Computer science, Optimization, Performance, Real-time graphics, Shaders, Virtual reality, Vulkan

August 11, 2018 by hgpu

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Raul Puri, Robert Kirby, Nikolai Yakovenko, Bryan Catanzaro

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Tags: Computer science, CUDA, Deep learning, LSTM, Mixed precision, Neural networks, nVidia, Package, Tesla V100, Torch

August 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

Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs

Linpeng Tang, Yida Wang, Theodore L. Willke, Kai Li

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Tags: Computer science, Deep learning, Intel Xeon Phi, Neural networks

August 5, 2018 by hgpu

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Francisco M. Castro, Nicolas Guil, Manuel J. Marin-Jimenez, Jesus Perez-Serrano, Manuel Ujaldon

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Tags: Artificial intelligence, CNN, Computer science, Computer vision, CUDA, Deep learning, Energy-efficient computing, Neural networks, nVidia, nVidia GeForce GTX Titan X

August 5, 2018 by hgpu

GPU schedulers: how fair is fair enough?

Tyler Sorensen, Hugues Evrard, Alastair F. Donaldson

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Tags: Computer science, HSA, OpenCL, Task scheduling

August 5, 2018 by hgpu

CRUM: Checkpoint-Restart Support for CUDA’s Unified Memory

Rohan Garg, Apoore Mohan, Michael Sullivan, Gene Cooperman

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Tesla P100

August 5, 2018 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

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high performance computing on graphics processing units: hgpu.org

A Qualitative Comparison Study Between Common GPGPU Frameworks

Kernel Tuner: A search-optimizing GPU code auto-tuner

Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

A Cross-platform Evaluation of Graphics Shader Compiler Optimization

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

GPU schedulers: how fair is fair enough?

CRUM: Checkpoint-Restart Support for CUDA’s Unified Memory

Recent source codes

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

SW#SYCL

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

HiRep

Gaia AVU-GSR

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

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