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Leveraging Memory Copy Overlap for Efficient Sparse Matrix-Vector Multiplication on GPUs

Guangsen Zeng, Yi Zou

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia A40, Sparse matrix

September 6, 2023 by hgpu

Increased reliability on Intel GPUs via software diverse redundancy

Nikolaos Andriotis

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Tags: Computer science, Deep learning, Machine learning, MPI, nVidia, OpenCL, Thesis

August 20, 2023 by hgpu

SYnergy: Fine-grained Energy-Efficient Heterogeneous Computing for Scalable Energy Saving

Kaijie Fan, Marco D'Antonio, Lorenzo Carpentieri, Biagio Cosenza, Federico Ficarelli, Daniele Cesarini

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, Energy-efficient computing, GPU cluster, Heterogeneous systems, Machine learning, nVidia, nVidia V100, SYCL

August 13, 2023 by hgpu

Isolated Scheduling for Distributed Training Tasks in GPU Clusters

Xinchi Han, Weihao Jiang, Peirui Cao, Qinwei Yang, Yunzhuo Liu, Shuyao Qi, Shengkai Lin, Shizhen Zhao

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Tags: Computer science, GPU cluster, Machine learning, Neural networks, nVidia, nVidia V100, Package

August 13, 2023 by hgpu

Efficiency without Tears: Securing Multilingual Programs with TRINITY

Hao Zhu, Baojian Hua, Xinrong Lin, Yufei Wu

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Tags: Computer science, Databases, Heterogeneous systems, HPC, Machine learning, OpenCL, Security

July 30, 2023 by hgpu

Creating a Dataset Supporting Translation Between OpenMP Fortran and C++ Code

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

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Tags: Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX 6000 Ada, OpenMP, Package

July 24, 2023 by hgpu

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Polykarpos Thomadakis

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Nuclear physics, nVidia, OpenCL, Physics, Task scheduling, Tesla V100, Thesis

July 16, 2023 by hgpu

cuSLINK: Single-linkage Agglomerative Clustering on the GPU

Corey J. Nolet, Divye Gala, Alex Fender, Mahesh Doijade, Joe Eaton, Edward Raff, John Zedlewski, Brad Rees, Tim Oates

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Tags: Algorithms, Cluster analysis, Clustering, Computer science, CUDA, Hierarchical clustering, Machine learning, Nearest neighbour, nVidia, nVidia A100, nVidia DGX-1, Package

July 2, 2023 by hgpu

DGEMM on Integer Matrix Multiplication Unit

Hiroyuki Ootomo, Katsuhisa Ozaki, Rio Yokota

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Tags: Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia Jetson AGX Orin, nVidia RTX 6000 Ada, nVidia Titan RTX, Package

June 25, 2023 by hgpu

Improving Performance of Iterative Applications through Interleaved Execution of Approximated CUDA Kernels

Gabriel Freytag

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Tags: Computer science, CUDA, Machine learning, Mixed precision, Neural networks, nVidia, nVidia A100, nVidia P100, Thesis

June 18, 2023 by hgpu

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

Gargi Alavani, Jineet Desai, Snehanshu Saha, Santonu Sarkar

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Machine learning, nVidia, Package, Tesla K20, Tesla M60, Tesla V100

June 11, 2023 by hgpu

Prediction of Performance and Power Consumption of GPGPU Applications

Gargi Alavani, Santonu Sarkar

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Tags: Computer science, CUDA, Energy-efficient computing, Java, Machine learning, nVidia, Package, Performance, PTX, Tesla K20, Thesis

May 14, 2023 by hgpu

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

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high performance computing on graphics processing units: hgpu.org

Leveraging Memory Copy Overlap for Efficient Sparse Matrix-Vector Multiplication on GPUs

Increased reliability on Intel GPUs via software diverse redundancy

SYnergy: Fine-grained Energy-Efficient Heterogeneous Computing for Scalable Energy Saving

Isolated Scheduling for Distributed Training Tasks in GPU Clusters

Efficiency without Tears: Securing Multilingual Programs with TRINITY

Creating a Dataset Supporting Translation Between OpenMP Fortran and C++ Code

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

cuSLINK: Single-linkage Agglomerative Clustering on the GPU

DGEMM on Integer Matrix Multiplication Unit

Improving Performance of Iterative Applications through Interleaved Execution of Approximated CUDA Kernels

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

Prediction of Performance and Power Consumption of GPGPU Applications

Recent source codes

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

HPX: a C++ Standard Library for Concurrency and Parallelism

TorchQC: Quantum Dynamics and Machine Learning

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

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