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Automatic Parallelization of Tiled Stencil Loop Nests on GPUs

Peng Di

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Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, Performance, Tesla C1060, Tesla C2050, Thesis

July 30, 2014 by hgpu

Aristotle: A Performance Impact Indicator for the OpenCL Kernels Using Local Memory

Jianbin Fang, Henk Sips, Ana Lucia Varbanescu

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Tags: ATI, ATI Radeon HD 7970, Computer science, Intel Xeon Phi, Local memory, nVidia, OpenCL, Performance, Portability, Tesla C1060, Tesla C2050, Tesla K20

July 29, 2014 by jfang

GPU Computing to Improve Game Engine Performance

Abu Asaduzzaman, Hin Y. Lee

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Tags: Computer science, CUDA, Data parallelism, Games, Image processing, nVidia, nVidia GeForce 8800 GTX, Performance

July 28, 2014 by hgpu

Performance-efficient mechanisms for managing irregularity in throughput processors

Minsoo Rhu

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 480, Performance, Prefetch, Thesis

July 20, 2014 by hgpu

Optimal Periods for Probing Convergence of Infinite-stage Dynamic Programmings on GPUs

Tsutomu Inamoto, Yoshinobu Higami, Shin-ya Kobayashi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Performance

July 17, 2014 by hgpu

On Development, Feasibility, and Limits of Highly Efficient CPU and GPU Programs in Several Fields

David Rohr

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Performance, Review, Thesis

July 14, 2014 by hgpu

GPU Techniques Applied to Euler Flow Simulations and Comparison to CPU Performance

Blake Koop

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Tags: CUDA, Fluid dynamics, nVidia, Performance, Tesla C2070

July 14, 2014 by hgpu

Benchmarking the Memory Hierarchy of Modern GPUs

Xinxin Mei, Kaiyong Zhao, Chengjian Liu, Xiaowen Chu

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 780, Package, Performance

July 14, 2014 by hgpu

Understanding the SIMD Efficiency of Graph Traversal on GPU

Yichao Cheng, Hong An, Zhitao Chen, Feng Li, Zhaohui Wang, Xia Jiang, Yi Peng

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Tesla C2050

July 10, 2014 by hgpu

Utilizing state-of-art NeuroES and GPGPU to optimize Mario AI

Hasse Lovgren

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Tags: ATI, ATI Radeon HD 5870, Computer science, Mathematics, Neural networks, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Performance, Tesla C1060, Thesis

July 9, 2014 by hgpu

On Static Timing Analysis of GPU Kernels

Vesa Hirvisalo

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Tags: Computer science, OpenCL, Performance, Timing analysis

July 4, 2014 by hgpu

SIMD Implementation of a Multiplicative Schwarz Smoother for a Multigrid Poisson Solver on an Intel Xeon Phi Coprocessor

Masatoshi Kawai, Takeshi Iwashita, Hiroshi Nakashima

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Tags: Computer science, Intel Xeon Phi, Performance, Poisson equation

July 4, 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

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high performance computing on graphics processing units: hgpu.org

Automatic Parallelization of Tiled Stencil Loop Nests on GPUs

Aristotle: A Performance Impact Indicator for the OpenCL Kernels Using Local Memory

GPU Computing to Improve Game Engine Performance

Performance-efficient mechanisms for managing irregularity in throughput processors

Optimal Periods for Probing Convergence of Infinite-stage Dynamic Programmings on GPUs

On Development, Feasibility, and Limits of Highly Efficient CPU and GPU Programs in Several Fields

GPU Techniques Applied to Euler Flow Simulations and Comparison to CPU Performance

Benchmarking the Memory Hierarchy of Modern GPUs

Understanding the SIMD Efficiency of Graph Traversal on GPU

Utilizing state-of-art NeuroES and GPGPU to optimize Mario AI

On Static Timing Analysis of GPU Kernels

SIMD Implementation of a Multiplicative Schwarz Smoother for a Multigrid Poisson Solver on an Intel Xeon Phi Coprocessor

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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