high performance computing on graphics processing units: hgpu.org

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A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

Pietro Incardona, Aryaman Gupta, Serhii Yaskovets, Ivo F. Sbalzarini

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Tags: AMD RX Vega 64, ATI, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, OpenACC, OpenCL, OpenMP, Package, Performance, performance portability, SYCL

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

ProtoX: A First Look

Het Mankad, Sanil Rao, Brian Van Straalen, Phillip Colella, Franz Franchetti

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Tags: Code generation, Computer science, Differential equations, Laplace and Poisson equation, Mathematical Software, Partial differential equations, PDEs, Poisson equation, Stencil computation

July 24, 2023 by hgpu

qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers

Hanyan Cao, Feng Pan, Yijia Wang, Pan Zhang

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Tags: Artificial intelligence, Computer science, Neural networks, nVidia, nVidia A100, Package

July 24, 2023 by hgpu

Maximizing Parallelism and GPU Utilization For Direct GPU Compilation Through Ensemble Execution

Shilei Tian, Barbara Chapman, Johannes Doerfert

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Tags: Benchmarking, Computer science, CUDA, LLVM, nVidia, nVidia A100, OpenMP, Performance

July 24, 2023 by hgpu

eGPU: A 750 MHz Class Soft GPGPU for FPGA

Martin Langhammer, George Constantinides

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Tags: Computer science, DSP, FPGA, Hardware Architecture

July 24, 2023 by hgpu

Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks

Mingyu Liang, Wenyin Fu, Louis Feng, Zhongyi Lin, Pavani Panakanti, Shengbao Zheng, Srinivas Sridharan, Christina Delimitrou

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Tags: AI, Benchmarking, Code generation, Computer science, CUDA, nVidia, Package, Performance, PyTorch, Tesla A100, Tesla V100

July 16, 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

Tile-based Lightweight Integer Compression in GPU

Anil Shanbhag, Bobbi W. Yogatama, Xiangyao Yu, Samuel Madden

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia V100, Package

July 16, 2023 by hgpu

Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU

Zhihe Zhao, Neiwen Ling, Nan Guan, Guoliang Xing

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce RTX 2060, nVidia Jetson TX2

July 16, 2023 by hgpu

Improving the Performance, Portability, and Productivity of Hardware Accelerators

Pablo Antonio Martínez Sánchez

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Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 2080 Ti, Performance, performance portability, Thesis

July 16, 2023 by hgpu

Safe, Seamless, And Scalable Integration Of Asynchronous GPU Streams In PETSc

Jacob Faibussowitsch, Mark F. Adams, Richard Tran Mills, Stefano Zampini, Junchao Zhang

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Tags: Computer science, CUDA, nVidia, nVidia A100, Package, Performance

July 9, 2023 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

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

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Applications

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

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

ProtoX: A First Look

qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers

Maximizing Parallelism and GPU Utilization For Direct GPU Compilation Through Ensemble Execution

eGPU: A 750 MHz Class Soft GPGPU for FPGA

Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Tile-based Lightweight Integer Compression in GPU

Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU

Improving the Performance, Portability, and Productivity of Hardware Accelerators

Safe, Seamless, And Scalable Integration Of Asynchronous GPU Streams In PETSc

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

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

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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