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A Hybrid Framework for Fast and Accurate GPU Performance Estimation through Source-Level Analysis and Trace-Based Simulation

Xiebing Wang, Kai Huang, Alois Knoll, Xuehai Qian

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Tags: Computer science, nVidia, nVidia GeForce GTX 645, nVidia GeForce GTX 940 M, nVidia Quadro K600, nVidia Quadro K620, OpenCL, Performance

May 26, 2019 by hgpu

Performance Analysis of Deep Learning Workloads on Leading-edge Systems

Yihui Ren, Shinjae Yoo, Adolfy Hoisie

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, Performance, Tesla V100

May 23, 2019 by hgpu

A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL

Yonghae Kim, Hyesoon Kim

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Tags: Code generation, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, OpenCL

May 23, 2019 by hgpu

Instructions’ Latencies Characterization for NVIDIA GPGPUs

Yehia Arafa, Abdel-Hameed Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan Eidenbenz

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia Titan RTX, Performance, PTX, Tesla K40, Tesla P100, Tesla V100

May 23, 2019 by hgpu

Neural Query Language: A Knowledge Base Query Language for Tensorflow

William W. Cohen, Matthew Siegler, Alex Hofer

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Tags: Computer science, Databases, Deep learning, Package, Performance

May 19, 2019 by hgpu

Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems

Karan Aggarwal, Uday Bondhugula, Varsha Sreenivasan, Devarajan Sridharan

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Tags: Algorithms, Computer science, CUDA, Neuroscience, nVidia, nVidia GeForce RTX 2080 Ti, Sparse matrix

May 19, 2019 by hgpu

Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL

Corey Lammie, Wei Xiang, Mostafa Rahimi Azghadi

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks, OpenCL

May 19, 2019 by hgpu

OpenDNN: An Open-source, cuDNN-like Deep Learning Primitive Library

Daeyeon Kim

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Tags: Computer science, Deep learning, FPGA, Neural networks, OpenCL, Performance, Thesis

May 19, 2019 by hgpu

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

Benjamin Ferrell, Jun Duan, Kevin W. Hamlen

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Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, PTX

May 15, 2019 by hgpu

A Unified Approach to Variable Renaming for Enhanced Vectorization

Prasanth Chatarasi, Jun Shirako, Albert Cohen, Vivek Sarkar

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Tags: Benchmarking, Compilers, Computer science, CUDA, Intel Xeon Phi, nVidia, Tesla V100

May 15, 2019 by hgpu

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Blanco Alonso, James King, Michael Hines, Felix Schuermann

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Tags: Biophysics, Code generation, Compilers, Computer science, CUDA, HPC, Intel Xeon Phi, LLVM, nVidia, OpenACC, OpenMP, Package

May 15, 2019 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

A Hybrid Framework for Fast and Accurate GPU Performance Estimation through Source-Level Analysis and Trace-Based Simulation

A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL

Instructions’ Latencies Characterization for NVIDIA GPGPUs

Neural Query Language: A Knowledge Base Query Language for Tensorflow

Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems

Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL

OpenDNN: An Open-source, cuDNN-like Deep Learning Primitive Library

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

A Unified Approach to Variable Renaming for Enhanced Vectorization

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

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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