high performance computing on graphics processing units: hgpu.org

Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, Tesla V100

February 6, 2022 by hgpu

SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets

Xiaodong Yu, Sheng Di, Kai Zhao, jiannan Tian, Dingwen Tao, Xin Liang, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia A100, Tesla V100

February 6, 2022 by hgpu

Porting OpenACC to OpenMP on heterogeneous systems

Hichan Agueny

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Tags: Computer science, Heterogeneous systems, Laplace and Poisson equation, nVidia, OpenACC, Tesla P100

February 6, 2022 by hgpu

GC3: An Optimizing Compiler for GPU Collective Communication

Meghan Cowan, Saeed Maleki, Madanlal Musuvathi, Olli Saarikivi, Yifan Xiong

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Tags: Algorithms, Compilers, Computer science, CUDA, Machine learning, nVidia, nVidia A100

February 6, 2022 by hgpu

Teaching Parallel Programming in Containers: Virtualization of a Heterogeneous Local Infrastructure

Naylor Garcia Bachiega

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, nVidia, nVidia GeForce GTX 650, Package, Thesis, Virtualization

January 30, 2022 by hgpu

Performance prediction of deep learning applications training in GPU as a service systems

Marco Lattuada, Eugenio Gianniti, Danilo Ardagna, Li Zhang

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Tags: Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Performance, Tesla K80, Tesla M60

January 30, 2022 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

A Comprehensive Benchmark of Deep Learning Libraries on Mobile Devices

Electrical-Level Attacks on CPUs, FPGAs, and GPUs: Survey and Implications in the Heterogeneous Era

Pattern-based Programming Abstractions for Heterogeneous Parallel Computing

FC_ACCEL: Enabling Efficient, Low-Latency and Flexible Inference in DNN Fully Connected Layers, using Optimized Checkerboard Block matrix decomposition, fast scheduling, and a resource efficient 1D PE array with a custom HBM2 memory subsystem

The Ecological Footprint of Neural Machine Translation Systems

Improving Loop Parallelization by a Combination of Static and Dynamic Analyses in HLS

Flashlight: Enabling Innovation in Tools for Machine Learning

SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets

Porting OpenACC to OpenMP on heterogeneous systems

GC3: An Optimizing Compiler for GPU Collective Communication

Teaching Parallel Programming in Containers: Virtualization of a Heterogeneous Local Infrastructure

Performance prediction of deep learning applications training in GPU as a service systems

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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