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Performance Debugging Frameworks for FPGA High-Level Synthesis

Young-kyu Choi

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Tags: Benchmarking, Computer science, Design space exploration, FPGA, HLS, Performance, Thesis

October 27, 2019 by hgpu

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

Mohammed Alser, Taha Shahroodi, Juan Gomez-Luna, Can Alkan, Onur Mutlu

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, Package, Sequence alignment

October 27, 2019 by hgpu

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

Filip Petrovič, David Střelák, Jana Hozzová, Jaroslav Oľha, Richard Trembecký, Siegfried Benkner, Jiří Filipovič

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Tags: AMD Radeon RX Vega 56, Auto-Tuning, Benchmarking, Computer science, CUDA, Electron microscopy, Intel Xeon Phi, Microscopy, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, performance portability, Tesla K20

October 27, 2019 by hgpu

AI Benchmark: All About Deep Learning on Smartphones in 2019

Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, Luc Van Gool

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Tags: Android, Benchmarking, Computer science, CUDA, Deep learning, nVidia, OpenCL, Overview

October 20, 2019 by hgpu

Blink: Fast and Generic Collectives for Distributed ML

Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil Devanur, Ion Stoica

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, Machine learning, nVidia, nVidia DGX-1, Tesla V100

October 20, 2019 by hgpu

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Ilia Sivkov, Alfio Lazzaro, Jurg Hutter

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Tags: Computer science, CUDA, Data mining, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Signal processing, Sparse matrix, Tesla P100

October 20, 2019 by hgpu

Characterizing Deep Learning Training Workloads on Alibaba-PAI

Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei Lin, Yangqing Jia

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Tags: Artificial intelligence, Computer science, Computer vision, Deep learning, GPU cluster, nVidia, Tesla V100

October 20, 2019 by hgpu

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Hamid Reza Zohouri, Satoshi Matsuoka

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Tags: Benchmarking, Computer science, FPGA, HLS, Memory, nVidia, OpenCL, Package, Tesla K20, Tesla V100

October 20, 2019 by hgpu

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Ameer M.S. Abdelhadi, Christos-Savvas Bouganis, George A. Constantinides

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Tags: Computer science, FPGA, Information Retrieval, Machine learning, Nearest neighbour, Package

October 13, 2019 by hgpu

Performance Evaluation of Blocking and NonBlocking Concurrent Queues on GPUs

Hossein Pourmeidani

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Tags: AMD Radeon R7, ATI, Benchmarking, Computer science, OpenCL, Performance, Thesis

October 13, 2019 by hgpu

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Valentin Radu, Kuba Kaszyk, Yuan Wen, Jack Turner, Jose Cano, Elliot J. Crowley, Bjorn Franke, Amos Storkey, Michael O'Boyle

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia Jetson TX2, OpenCL

October 13, 2019 by hgpu

hlslib: Software Engineering for Hardware Design

Johannes de Fine Licht, Torsten Hoefler

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Tags: Computer science, FPGA, OpenCL, Package, Software Engineering

October 13, 2019 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

Applications

Performance Debugging Frameworks for FPGA High-Level Synthesis

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

AI Benchmark: All About Deep Learning on Smartphones in 2019

Blink: Fast and Generic Collectives for Distributed ML

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Characterizing Deep Learning Training Workloads on Alibaba-PAI

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Performance Evaluation of Blocking and NonBlocking Concurrent Queues on GPUs

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

hlslib: Software Engineering for Hardware Design

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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