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A characterization and analysis of PTX kernels

Andrew Kerr, Gregory Diamos, Sudhakar Yalamanchili

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance, PTX

April 2, 2011 by hgpu

Ocelot: a dynamic optimization framework for bulk-synchronous applications in heterogeneous systems

Gregory F. Diamos, Andrew R. Kerr, Sudhakar Yalamanchili, Nathan Clark

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Programming Languages, PTX

March 9, 2011 by hgpu

Analyzing CUDA workloads using a detailed GPU simulator

Ali Bakhoda, George L. Yuan, Wilson W. L. Fung, Henry Wong, Tor M. Aamodt

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8600 GTS, PTX

October 28, 2010 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

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

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

Can Large Language Models Predict Parallel Code Performance?

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

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

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

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

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