high performance computing on graphics processing units: hgpu.org

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Depth Map Based Superresolution Method in 3D Reconstruction

Gabriel Bernardino

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, Tesla C1060, Thesis

February 5, 2014 by hgpu

A Real-Time, GPU-Based, Non-Imaging Back-End for Radio Telescopes

Alessio Magro

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Tags: Astrophysics, Clustering, CUDA, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, nVidia, Tesla K20, Thesis

February 4, 2014 by hgpu

Parallelization of the Algorithm WHAM with NVIDIA CUDA

Nicolo' Savioli

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Tags: Algorithms, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GT 320 M, Physics, Thesis

February 2, 2014 by hgpu

Towards Efficient Risk Quantification-Using GPUs and Variance Reduction Technique

Shih Hau Tan

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Tags: CUDA, Finance, nVidia, nVidia GeForce GTX 680, Thesis

January 30, 2014 by hgpu

A Novel Graphical Processing Unit Method for Power Systems Security Analysis

Laurie Elizabeth Miller

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, Security, Sparse matrix, Thesis

January 30, 2014 by hgpu

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Gary Macindoe

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Machine learning, Matrix decomposition, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla S1070, Thesis

January 29, 2014 by hgpu

Impact of communication times on mixed CPU/GPU applications scheduling using KAAPI

David Beniamine

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Tags: Computer science, CUDA, nVidia, nVidia Quadro NVS 300, Task scheduling, Tesla C2050, Thesis

January 29, 2014 by hgpu

Scheduling on Manycore and Heterogeneous Graphics Processors

Stanley Tzeng

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenCL, Thesis

January 28, 2014 by hgpu

Automatic Resource-Constrained Static Task Parallelization

Dounia Khaldi

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Tags: Algorithms, Code generation, Computer science, MPI, OpenCL, Task scheduling, Thesis

January 28, 2014 by hgpu

Performance-Correctness Challenges in Emerging Heterogeneous Multicore Processors

Vineeth Thamarassery Mekkat

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, nVidia GeForce 8600 GTS, Thesis

January 28, 2014 by hgpu

Autotuning Programs with Algorithmic Choice

Jason Ansel

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Tags: Algorithms, ATI, ATI Radeon HD 6630 M, Computer science, nVidia, OpenCL, Package, Performance, Tesla C2070, Thesis

January 28, 2014 by hgpu

Low-latency Image Recognition with GPU-accelerated Convolutional Networks for Web-based Services

Fu Jie Huang

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Tags: Cloud, CUDA, Image processing, Image recognition, nVidia, nVidia GeForce GTX 560 Ti, Thesis

January 26, 2014 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Depth Map Based Superresolution Method in 3D Reconstruction

A Real-Time, GPU-Based, Non-Imaging Back-End for Radio Telescopes

Parallelization of the Algorithm WHAM with NVIDIA CUDA

Towards Efficient Risk Quantification-Using GPUs and Variance Reduction Technique

A Novel Graphical Processing Unit Method for Power Systems Security Analysis

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Impact of communication times on mixed CPU/GPU applications scheduling using KAAPI

Scheduling on Manycore and Heterogeneous Graphics Processors

Automatic Resource-Constrained Static Task Parallelization

Performance-Correctness Challenges in Emerging Heterogeneous Multicore Processors

Autotuning Programs with Algorithmic Choice

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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

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