high performance computing on graphics processing units: hgpu.org

Theses

hgpu.org » Applications

Addressing Challenges in Utilizing GPUs for Accelerating Privacy-Preserving Computation

Ardhi Wiratama Baskara Yudha

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Security, Thesis

June 2, 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

Direct Numerical Simulation of Turbulence on Heterogenous Computer Systems: Architectures, Algorithms, and Applications

Martin Karp

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Tags: AMD Radeon Instinct MI250X, ATI, cfd, Fluid dynamics, FPGA, Heterogeneous systems, Numerical simulation, nVidia, nVidia A100, nVidia P100, nVidia V100, OpenCL, Spectral elements, Thesis

May 12, 2024 by hgpu

Software Optimization and Orchestration for Heterogeneous and Distributed Architectures

Francesco Lumpp

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, Performance, Thesis

April 21, 2024 by hgpu

Deductive verification for SYCL

Ellen Wittingen

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Tags: Computer science, CUDA, Heterogeneous systems, HIP, nVidia, OpenCL, OpenMP, Package, SYCL, Thesis

February 4, 2024 by hgpu

Deep Learning for Obfuscated Code Analysis

Alexander Shroyer

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 2060, OpenCL, Package, PyTorch, Thesis

January 7, 2024 by hgpu

Adding fault tolerance to OpenCL: Through redundant heterogeneous computing

Robin Alexander Bijl

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

December 31, 2023 by hgpu

Comparative Performance and Scalability Analysis of GPU-accelerated Database Operations

Carl Andersson, Jonathan Nilsson

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Tags: Benchmarking, Computer science, CUDA, Databases, nVidia, nVidia A10, nVidia M60, Performance, Tesla T4, Thesis

December 24, 2023 by hgpu

Compiler-centric across-stack deep learning acceleration

Perry Gibson

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Tags: Compilers, Computer science, Deep learning, Design space exploration, nVidia, nVidia Jetson AGX Xavier, nVidia Titan RTX, Thesis

December 10, 2023 by hgpu

Testing and Mutation Testing for GPU Kernels

Yujie Liu

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Tags: Benchmarking, Computer science, CUDA, nVidia, Package, Performance, Thesis

December 3, 2023 by hgpu

Evaluation of FPGA-based high performance computing platforms

Martin Frick-Lundgren

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Tags: Computer science, FPGA, HPC, Intel, Intel UHD 630, Matrix multiplication, OpenCL, Package, SYCL, Thesis

November 27, 2023 by hgpu

Accelerating bioinformatics applications on CUDA-enabled multi-GPU systems

Robin Kobus

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Tags: Bioinformatics, Biology, CUDA, Databases, Hashing, nVidia, nVidia Quadro GV100, Package, Sequence alignment, Tesla P100, Tesla V100, Thesis

November 27, 2023 by hgpu

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

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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

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high performance computing on graphics processing units: hgpu.org

Theses

Addressing Challenges in Utilizing GPUs for Accelerating Privacy-Preserving Computation

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Direct Numerical Simulation of Turbulence on Heterogenous Computer Systems: Architectures, Algorithms, and Applications

Software Optimization and Orchestration for Heterogeneous and Distributed Architectures

Deductive verification for SYCL

Deep Learning for Obfuscated Code Analysis

Adding fault tolerance to OpenCL: Through redundant heterogeneous computing

Comparative Performance and Scalability Analysis of GPU-accelerated Database Operations

Compiler-centric across-stack deep learning acceleration

Testing and Mutation Testing for GPU Kernels

Evaluation of FPGA-based high performance computing platforms

Accelerating bioinformatics applications on CUDA-enabled multi-GPU systems

Recent source codes

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

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

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