high performance computing on graphics processing units: hgpu.org

Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes

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Tags: Algorithms, Computer science, Machine learning, Neural networks, nVidia, Package, TensorFlow, Tesla V100

May 5, 2019 by hgpu

Evaluating the Arm Ecosystem for High Performance Computing

Adrian Jackson, Andrew Turner, Michele Weiland, Nick Johnson, Olly Perks, Mark Parsons

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Tags: ARM, Benchmarking, Computer science, Distributed computing, HPC, Performance

May 1, 2019 by hgpu

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

Heinrich Riebler, Gavin Vaz, Tobias Kenter, Christian Plessl

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Tags: Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Tesla K20

May 1, 2019 by hgpu

The Risks of WebGL: Analysis, Evaluation and Detection

Alex Belkin, Nethanel Gelernter, Israel Cidon

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Tags: Computer science, nVidia, nVidia GeForce GTX 1080, Security, WebGL

May 1, 2019 by hgpu

A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

James D. Stevens

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Tags: AMD Radeon R9 Fury, ATI, Code generation, Computer science, Finite difference, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 28, 2019 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Applications

Charactering and Detecting CUDA Program Bugs

FPGA-based acceleration of a particle simulation High Performance Computing application

TensorNetwork: A Library for Physics and Machine Learning

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

An Architectural Journey into RISC Architectures for HPC Workloads

Full-stack Optimization for Accelerating CNNs with FPGA Validation

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

Evaluating the Arm Ecosystem for High Performance Computing

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

The Risks of WebGL: Analysis, Evaluation and Detection

A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

Recent source codes

OpScanner

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

INT v.s. FP: A framework to compare low-bit integer and float-point formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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