high performance computing on graphics processing units: hgpu.org

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Thermal Safety and Real-Time Predictability on Heterogeneous Embedded SoC Platforms

Seyed Mehdi Hosseini Motlagh

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Tags: Computer science, DSP, Heterogeneous systems, SoC, Thesis

January 3, 2021 by hgpu

Exploiting BSP Abstractions for Compiler Based Optimizations of GPU Applications on multi-GPU Systems

Alexander Matz

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Tags: Benchmarking, Code generation, Compilers, Computer science, CUDA, nVidia, Package, Thesis

December 27, 2020 by hgpu

Directive-Based Data Partitioning and Pipelining and Auto-Tuning for High-Performance GPU Computing

Xuewen Cui

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Tags: Artificial intelligence, Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, OpenMP, Performance, Tesla K40, Tesla P100, Tesla V100, Thesis

December 20, 2020 by hgpu

Efficient code generation for hardware accelerators by refining partially specified implementation

Ulysse Beaugnon

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Tags: Algorithms, Code generation, Computer science, CUDA, Linear Algebra, nVidia, Optimization, Package, Thesis

December 13, 2020 by hgpu

Efficient Deep Neural Network Inference for Embedded Systems: A Mixture of Experts Approach

Benjamin David Taylor

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Tags: Algorithms, Cloud, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson TX2, Thesis

November 29, 2020 by hgpu

Adaptive Data Migration in Load-Imbalanced HPC Applications

Parsa Amini

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, load balancing, nVidia, Package, Tesla P100, Tesla V100, Thesis

November 15, 2020 by hgpu

Automatic GPU optimization through higher-order functions in functional languages

John Wikman

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Tags: Computer science, CUDA, nVidia, nVidia Quadro P 2000, Optimization, Thesis

November 15, 2020 by hgpu

Designing a Modern Skeleton Programming Framework for Parallel and Heterogeneous Systems

August Ernstsson

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, nVidia, OpenCL, Tesla K20, Thesis

November 1, 2020 by hgpu

Adaptation of High Performance and High Capacity Reconfigurable Systems to OpenCL Programming Environments

Davide Russo

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, Thesis

September 27, 2020 by hgpu

Designing Efficient Barriers and Semaphores for Graphics Processing Units

Rohan Mahapatra

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2080 Ti, Thesis

September 20, 2020 by hgpu

A Comparison of Optimal Scanline Voxelization Algorithms

Tim Håkansson

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Tags: 3D Graphics and Realism, CUDA, GLSL, nVidia, OpenGL, Package, Tesla T4, Thesis, Voxelization

September 20, 2020 by hgpu

Array-Oriented Languages and Polyhedral Compilation

Justin Paston-Cooper

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Tags: Computer science, High-level Languages, nVidia, nVidia GeForce RTX 2080, Thesis

September 6, 2020 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Thermal Safety and Real-Time Predictability on Heterogeneous Embedded SoC Platforms

Exploiting BSP Abstractions for Compiler Based Optimizations of GPU Applications on multi-GPU Systems

Directive-Based Data Partitioning and Pipelining and Auto-Tuning for High-Performance GPU Computing

Efficient code generation for hardware accelerators by refining partially specified implementation

Adaptive Data Migration in Load-Imbalanced HPC Applications

Automatic GPU optimization through higher-order functions in functional languages

Designing a Modern Skeleton Programming Framework for Parallel and Heterogeneous Systems

Adaptation of High Performance and High Capacity Reconfigurable Systems to OpenCL Programming Environments

Designing Efficient Barriers and Semaphores for Graphics Processing Units

A Comparison of Optimal Scanline Voxelization Algorithms

Array-Oriented Languages and Polyhedral Compilation

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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