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Verified Instruction-Level Energy Consumption Measurement for NVIDIA GPUs

Yehia Arafa, Ammar ElWazir, Abdelrahman ElKanishy, Youssef Aly, Ayatelrahman Elsayed, Abdel-Hameed Badawy, Gopinath Chennupati, Stephan Eidenbenz, Nandakishore Santhi

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Tags: Computer science, CUDA, Energy-efficient computing, Measurement techniques, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, nVidia Titan RTX, PTX

February 23, 2020 by hgpu

Let’s sort this out: GPGPU Verification of Radix Sort

Dré van Oorschot

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Tags: Algorithms, Computer science, Package, Sorting

February 23, 2020 by hgpu

From English To Foreign Languages: Transferring Pre-trained Language Models

Ke Tran

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Tags: Computer science, NLP, nVidia, Tesla V100

February 23, 2020 by hgpu

High-Performance High-Order Stencil Computation on FPGAs Using OpenCL

Hamid Reza Zohouri, Artur Podobas, Satoshi Matsuoka

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Tags: Computer science, FPGA, OpenCL, Stencil computation

February 23, 2020 by hgpu

The Deep Learning Compiler: A Comprehensive Survey

Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Depei Qian

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Tags: Compilers, Computer science, Deep learning, Machine learning, Performance

February 16, 2020 by hgpu

EASYPAP: a Framework for Learning Parallel Programming

Alice Lasserre, Raymond Namyst, Pierre-André Wacrenier

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Tags: Algorithms, AVX, Computer science, Education, MPI, OpenCL, OpenMP, Package, Pthreads

February 16, 2020 by hgpu

ISM2: Optimizing Irregular-Shaped Matrix-Matrix Multiplication on GPUs

Cody Rivera, Jieyang Chen, Nan Xiong, Shuaiwen Leon Song, Dingwen Tao

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Tags: Algorithms, Computer science, CUDA, GEMM, Linear Algebra, Matrix multiplication, nVidia, Tesla K40, Tesla M40, Tesla P100

February 16, 2020 by hgpu

Task-based, GPU-accelerated and Robust Library for Solving Dense Nonsymmetric Eigenvalue Problems

Mirko Myllykoski, Carl Christian Kjelgaard Mikkelsen

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Tags: Computer science, CUDA, Mathematical Software, nVidia, Package, Tesla V100

February 16, 2020 by hgpu

Working With Incremental Spatial Data During Parallel (GPU) Computation

Robert Chisholm

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Tags: Agent-based modeling, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan V, Package, Thesis

February 9, 2020 by hgpu

Automated Runtime Analysis and Adaptation for Scalable Heterogeneous Computing

Ahmed E. Helal

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Tags: Computer science, CUDA, HPC, nVidia, OpenCL, OpenMP, Package, Performance, Tesla M2050, Thesis

February 9, 2020 by hgpu

TC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory

Andi Drebes, Lorenzo Chelini, Oleksandr Zinenko, Albert Cohen, Henk Corporaal, Tobias Grosser, Kanishkan Vadivel, Nicolas Vasilache

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Tags: Compilers, Computer science, CUDA, Machine learning

February 9, 2020 by hgpu

MKPipe: A Compiler Framework for Optimizing Multi-Kernel Workloads in OpenCL for FPGA

Ji Liu, Abdullah-Al Kafi, Xipeng Shen, Huiyang Zhou

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Tags: Compilers, Computer science, FPGA, nVidia, nVidia GeForce RTX 2080, OpenCL, Performance

February 9, 2020 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Verified Instruction-Level Energy Consumption Measurement for NVIDIA GPUs

Let’s sort this out: GPGPU Verification of Radix Sort

From English To Foreign Languages: Transferring Pre-trained Language Models

High-Performance High-Order Stencil Computation on FPGAs Using OpenCL

The Deep Learning Compiler: A Comprehensive Survey

EASYPAP: a Framework for Learning Parallel Programming

ISM2: Optimizing Irregular-Shaped Matrix-Matrix Multiplication on GPUs

Task-based, GPU-accelerated and Robust Library for Solving Dense Nonsymmetric Eigenvalue Problems

Working With Incremental Spatial Data During Parallel (GPU) Computation

Automated Runtime Analysis and Adaptation for Scalable Heterogeneous Computing

TC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory

MKPipe: A Compiler Framework for Optimizing Multi-Kernel Workloads in OpenCL for FPGA

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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