high performance computing on graphics processing units: hgpu.org

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Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

L.A. Torres, Carlos J. Barrios H, Yves Denneulin

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia A100, Package, Performance, SYCL

June 2, 2024 by hgpu

A Survey of Cloud-Based GPU Threats and Their Impact on AI, HPC, and Cloud Computing

Numaan Huq, Philippe Lin, Roel Reyes, Charles Perine

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Tags: AMD Radeon Pro V520, Artificial intelligence, ATI, Cloud, Computer science, CUDA, Deep learning, nVidia, OpenCL, Security, Tesla T4

June 2, 2024 by hgpu

Enabling full-speed random access to the entire memory on the A100 GPU

Alden Walker

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Tags: Computer science, nVidia, nVidia A100, Performance, PTX

May 26, 2024 by hgpu

GPU Implementations for Midsize Integer Addition and Multiplication

Cosmin E. Oancea, Stephen M. Watt

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia A100, Performance, Programming Languages

May 26, 2024 by hgpu

STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning

Roberto L. Castro, Diego Andrade, Basilio B. Fraguela

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia A100, Tesla T4

May 26, 2024 by hgpu

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Zhaodong Chen

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia A100, nVidia V100, Thesis

May 26, 2024 by hgpu

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

Urvij Saroliya, Eishi Arima, Dai Liu, Martin Schulz

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia A100, PC cluster, Task scheduling

May 20, 2024 by hgpu

Workload Scheduling on Heterogeneous Devices

Harsh Khetawat, Frank Mueller

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Tags: Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce RTX 2060, OpenCL, Task scheduling

May 20, 2024 by hgpu

Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls

Sicong Liu, Wentao Zhou, Zimu Zhou, Bin Guo, Minfan Wang, Cheng Fang, Zheng Lin, Zhiwen Yu

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Tags: Computer science, Deep learning, Heterogeneous systems

May 12, 2024 by hgpu

Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps

Eishi Arima, Minjoon Kang, Issa Saba, Josef Weidendorfer, Carsten Trinitis, Martin Schulz

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Tags: Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia A100, Performance

May 12, 2024 by hgpu

CuPBoP: Making CUDA a Portable Language

Ruobing Han, Jun Chen, Bhanu Garg, Xule Zhou, John Lu, Jeffrey Young, Jaewoong Sim, Hyesoon Kim

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Tags: Computer science, CUDA, HIP, nVidia, nVidia A30, OpenCL, Package, Portability, SYCL

May 12, 2024 by hgpu

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Faial: finds bugs in CUDA kernels

Sound and Partially-Complete Static Analysis of Data-Races in GPU Programs

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

UVaFTLE: Lagrangian finite time Lyapunov exponent extraction for fluid dynamic applications

Challenging Portability Paradigms: FPGA Acceleration Using SYCL and OpenCL

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high performance computing on graphics processing units: hgpu.org

Applications

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

Enabling full-speed random access to the entire memory on the A100 GPU

GPU Implementations for Midsize Integer Addition and Multiplication

STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

Workload Scheduling on Heterogeneous Devices

Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls

Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps

CuPBoP: Making CUDA a Portable Language

Recent source codes

EnergyUCB-Bandit

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Faial: finds bugs in CUDA kernels

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

Intel® SHMEM: Device initiated shared memory based communication library

miniLB: Lattice Botlzmann miniapp w/SYCL

AFOCL

2domination

MFC: Exascale simulation of multiphase/physics fluid dynamics

UVaFTLE: Lagrangian finite time Lyapunov exponent extraction for fluid dynamic applications

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