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Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

Jiaquan Gao, Panpan Qi, Guixia He

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla C2050, Tesla K20

October 4, 2016 by hgpu

APL on GPUs: A TAIL from the Past, Scribbled in Futhark

Troels Henriksen, Martin Dybdal, Henrik Urms, Anna Sofie Kiehn, Daniel Gavin, Hjalte Abelskov, Martin Elsman, Cosmin Oancea

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Tags: Compilers, Computer science, nVidia, nVidia GeForce GTX 780 Ti, OpenCL, Package, PyOpenCL, Python, Visualization

October 4, 2016 by hgpu

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Graham Taylor, Shawki Areibi

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Tags: Benchmarking, Caffe, Computer science, Deep learning, FPGA, Machine learning, Neural networks, OpenCL, Package

October 4, 2016 by hgpu

Training a Feedback Loop for Hand Pose Estimation

Markus Oberweger, Paul Wohlhart, Vincent Lepetit

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Tags: Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX 780 Ti, Package, Theano

October 4, 2016 by hgpu

Explicit Fourth-Order Runge-Kutta Method on Intel Xeon Phi Coprocessor

Beata Bylina, Joanna Potiopa

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, OpenMP, Sparse matrix

October 4, 2016 by hgpu

GPU-based timetable generation

Ricardo Filipe Pereira Neves

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Tags: Computer science, CUDA, Genetic algorithm, nVidia, nVidia GeForce GTX 770, Thesis

September 30, 2016 by hgpu

Programming Models and Tools for Many-Core Platforms

Alessandro Capotondi

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Tags: Computer science, Heterogeneous systems, OpenCL, OpenMP, SoC, Thesis

September 30, 2016 by hgpu

Distributed Training of Deep Neuronal Networks: Theoretical and Practical Limits of Parallel Scalability

Janis Keuper

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Tags: Caffe, Computer science, CUDA, Deep learning, Intel Xeon Phi, Neural networks, nVidia, Tesla K80

September 30, 2016 by hgpu

Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs

R. Tapiador, A. Rios-Navarro, A. Linares-Barranco, Minkyu Kim, Deepak Kadetotad, Jae-sun Seo

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Tags: Artificial intelligence, Caffe, Computer science, Deep learning, FPGA, Neural networks, OpenCL

September 30, 2016 by hgpu

Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform

Sebastian Cammerer, Benedikt Leible, Matthias Stahl, Jakob Hoydis, Stephan ten Brink

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980 Ti, Signal processing

September 30, 2016 by hgpu

FastCollect: Offloading Generational Garbage Collection to Integrated GPUs

Abhinav, Rupesh Nasre

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Tags: Algorithms, Benchmarking, Computer science, Intel HD Graphics 4000, Java, OpenCL, Performance

September 27, 2016 by hgpu

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

September 27, 2016 by hgpu

Specx: Speculative task-based runtime system

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

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

KISim: Kubernetes Intelligent Scheduling Simulator

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

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Efficient GPU Implementation of Multi-Precision Integer Division

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

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

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

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

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high performance computing on graphics processing units: hgpu.org

Applications

Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

APL on GPUs: A TAIL from the Past, Scribbled in Futhark

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

Training a Feedback Loop for Hand Pose Estimation

Explicit Fourth-Order Runge-Kutta Method on Intel Xeon Phi Coprocessor

GPU-based timetable generation

Programming Models and Tools for Many-Core Platforms

Distributed Training of Deep Neuronal Networks: Theoretical and Practical Limits of Parallel Scalability

Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs

Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform

FastCollect: Offloading Generational Garbage Collection to Integrated GPUs

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Recent source codes

Specx: Speculative task-based runtime system

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

KISim: Kubernetes Intelligent Scheduling Simulator

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Efficient GPU Implementation of Multi-Precision Integer Division

ParEval: A Parallel Code Evaluation Benchmark

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

exa-AMD: Exascale Accelerated Materials Discovery

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

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