high performance computing on graphics processing units: hgpu.org

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Compressed Real Numbers for AI: a case-study using a RISC-V CPU

Federico Rossi, Marco Cococcioni, Roger Ferrer Ibàñez, Jesùs Labarta, Filippo Mantovani, Marc Casas, Emanuele Ruffaldi, Sergio Saponara

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Tags: AI, Compression, Computer science, Machine learning, Neural networks

September 24, 2023 by hgpu

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

CATO: automatic source transformation to apply HPC frameworks with minimal user interaction

Compiler-assisted distribution of OpenMP code for improved scalability

ADIOS2.jl: a Julia interface to ADIOS2

Julia as a unifying end-to-end workflow language on the Frontier exascale system

Code generated HPC Kernels Using Llama-2-70b

Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation

Scope is all you need: Transforming LLMs for HPC Code

pocl - Portable Computing Language

PoCL-R: An Open Standard Based Offloading Layer for Heterogeneous Multi-Access Edge Computing with Server Side Scalability

HPAC-Offload: Accelerating HPC Applications with Portable Approximate Computing on the GPU

openCLDispatcher

Novel insights on atomic synchronization for sort-based group-by on GPUs

CEED Benchmarks

Performant low-order matrix-free finite element kernels on GPU architectures

Sieve: Stratified GPU-Compute Workload Sampling

EngineCL: Usability and Performance in Heterogeneous Computing

Mashing load balancing algorithm to boost hybrid kernels in molecular dynamics simulations

See all packages

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high performance computing on graphics processing units: hgpu.org

Compressed Real Numbers for AI: a case-study using a RISC-V CPU

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

Recent source codes

CATO: automatic source transformation to apply HPC frameworks with minimal user interaction

ADIOS2.jl: a Julia interface to ADIOS2

Code generated HPC Kernels Using Llama-2-70b

Scope is all you need: Transforming LLMs for HPC Code

pocl - Portable Computing Language

HPAC-Offload: Accelerating HPC Applications with Portable Approximate Computing on the GPU

openCLDispatcher

CEED Benchmarks

Sieve: Stratified GPU-Compute Workload Sampling

EngineCL: Usability and Performance in Heterogeneous Computing

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