high performance computing on graphics processing units: hgpu.org

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Assessing opportunities of SYCL for biological sequence alignment on GPU-based systems

Manuel Costanzo, Enzo Rucci, Carlos García-Sanchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: AMD Radeon RX 6700 XT, AMD Radeon RX Vega 6, ATI, Bioinformatics, Biology, Computational biology, CUDA, Heterogeneous systems, Intel Arc A770, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, nVidia V100, oneAPI, Package, Sequence alignment, SYCL

February 25, 2024 by hgpu

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures

Negar Alizadeh, Fernando Castor

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Tags: Computer science, CUDA, Deep learning, Energy-efficient computing, nVidia, nVidia GeForce RTX 3070, Package, PyTorch, TensorFlow

February 25, 2024 by hgpu

TransAxx: Efficient Transformers with Approximate Computing

Dimitrios Danopoulos, Georgios Zervakis, Dimitrios Soudris, Jörg Henkel

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia V100, Package, PyTorch

February 18, 2024 by hgpu

Graphtoy: Fast Software Simulation of Applications for AMD’s AI Engines

Jonathan Strobl, Leonardo Solis-Vasquez, Yannick Lavan, Andreas Koch

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Tags: ATI, Computer science, FPGA, molecular docking, Package

February 18, 2024 by hgpu

QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference

Taesu Kim, Jongho Lee, Daehyun Ahn, Sarang Kim, Jiwoong Choi, Minkyu Kim, Hyungjun Kim

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Mixed precision, nVidia, nVidia A100, nVidia GeForce RTX 4090, nVidia RTX A6000, Package

February 18, 2024 by hgpu

DeepSeek-Coder: When the Large Language Model Meets Programming – The Rise of Code Intelligence

Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y.K. Li, Fuli Luo, Yingfei Xiong, Wenfeng Liang

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Tags: Code generation, Computer science, nVidia, nVidia A100, Package

February 12, 2024 by hgpu

Gallatin: A General-Purpose GPU Memory Manager

Hunter McCoy, Prashant Pandey

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Tags: Computer science, CUDA, HPC, Memory, nVidia, nVidia A40, Package

February 4, 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

High-order thread-safe lattice Boltzmann model for HPC turbulent flow simulations

Andrea Montessori, Michele La Rocca, Giorgio Amati, Marco Lauricella, Adriano Tiribocchi, Sauro Succi

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Tags: cfd, CUDA, Fluid dynamics, HPC, Lattice Boltzmann model, nVidia, nVidia A100, nVidia GeForce RTX 3090, Package

February 4, 2024 by hgpu

LeftoverLocals: Listening to LLM Responses Through Leaked GPU Local Memory

Tyler Sorensen, Heidy Khlaaf

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Tags: Computer science, Data recovery, nVidia, nVidia GeForce RTX 4070, OpenCL, Package, Security, Vulkan

February 4, 2024 by hgpu

Towards a GPU-Parallelization of the neXtSIM-DG Dynamical Core

Robert Jendersie, Christian Lessig, Thomas Richter

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Tags: Computer science, CUDA, Earth and Space Sciences, Finite element method, Numerical Analysis, nVidia, nVidia A100, nVidia GeForce RTX 3090, OpenMP, Package, PyTorch, SYCL

February 4, 2024 by hgpu

Lessons Learned Migrating CUDA to SYCL: A HEP Case Study with ROOT RDataFrame

Jolly Chen, Monica Dessole, Ana Lucia Varbanescu

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Tags: CUDA, HEP, nVidia, nVidia Quadro RTX 4000, OpenCL, Package, Performance, Physics, SYCL

January 28, 2024 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

Packages

Assessing opportunities of SYCL for biological sequence alignment on GPU-based systems

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures

TransAxx: Efficient Transformers with Approximate Computing

Graphtoy: Fast Software Simulation of Applications for AMD’s AI Engines

QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference

DeepSeek-Coder: When the Large Language Model Meets Programming – The Rise of Code Intelligence

Gallatin: A General-Purpose GPU Memory Manager

Deductive verification for SYCL

High-order thread-safe lattice Boltzmann model for HPC turbulent flow simulations

LeftoverLocals: Listening to LLM Responses Through Leaked GPU Local Memory

Towards a GPU-Parallelization of the neXtSIM-DG Dynamical Core

Lessons Learned Migrating CUDA to SYCL: A HEP Case Study with ROOT RDataFrame

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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