high performance computing on graphics processing units: hgpu.org

Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, Peter Vajda, Matt Uyttendaele, Niraj K. Jha

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Tags: Bayesian, Computer science, CUDA, Deep learning, DSP, Neural networks, nVidia, nVidia GeForce GTX 1060

December 30, 2018 by hgpu

Performance Analysis of a Stereo Matching Implementation in OpenCL

Stephan Rotheneder

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 750 Ti, nVidia Quadro 1000 M, OpenCL, Stereo vision

December 16, 2018 by hgpu

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

KISim: Kubernetes Intelligent Scheduling Simulator

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling

Efficient GPU Implementation of Multi-Precision Integer Division

exa-AMD: Exascale Accelerated Materials Discovery

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing

ParEval: A Parallel Code Evaluation Benchmark

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

No More Shading Languages: Compiling C++ to Vulkan Shaders

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

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high performance computing on graphics processing units: hgpu.org

Anatomizing Deep Learning Inference in Web Browsers

A Framework to Generate High-Performance Time-stepped Agent-based Simulations on Heterogeneous Hardware

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs

Accelerating AutoDock VINA with GPUs

PoCL-R: A Scalable Low Latency Distributed OpenCL Runtime

CUDA Tutorial – Cryptanalysis of Classical Ciphers Using Modern GPUs and CUDA

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Pipelined Training with Stale Weights of Deep Convolutional Neural Networks

ALPyNA: Acceleration of Loops in Python for Novel Architectures

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation