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Accelerating High-Order Stencils on GPUs

Ryuichi Sai, John Mellor-Crummey, Xiaozhu Meng, Mauricio Araya-Polo, Jie Meng

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Tags: Computer science, HPC, nVidia, nVidia Quadro NVS 510, Package, Performance, Stencil computation, Tesla P100, Tesla V100

September 13, 2020 by hgpu

HPX – The C++ Standard Library for Parallelism and Concurrency

Hartmut Kaiser, Patrick Diehl, Adrian S. Lemoine, Bryce Adelstein Lelbach, Parsa Amini, Agustín Berge, John Biddiscombe, Steven R. Brandt, Nikunj Gupta, Thomas Heller, Kevin Huck, Zahra Khatami, Alireza Kheirkhahan, Auriane Reverdell, Shahrzad Shirzad, Mikael Simberg, Bibek Wagle, Weile Wei, Tianyi Zhang

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Performance

September 13, 2020 by hgpu

GPA: A GPU Performance Advisor Based on Instruction Sampling

Keren Zhou, Xiaozhu Meng, Ryuichi Sai, John Mellor-Crummey

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Tags: Benchmarking, Computer science, nVidia, Performance, Tesla V100

September 13, 2020 by hgpu

Hierarchical Roofline Analysis: How to Collect Data using Performance Tools on Intel CPUs and NVIDIA GPUs

Charlene Yang

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Tags: Computer science, CUDA, nVidia, Package, Performance, Tesla V100

September 13, 2020 by hgpu

Hash-Based Authentication Revisited in the Age of High-Performance Computers

Niclas Hedam, Jakob Mollerup, Pinar Tozun

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Tags: Computer science, CUDA, Databases, Hashing, nVidia, nVidia GeForce RTX 2070, OpenCL, Security, Tesla V100

September 6, 2020 by hgpu

Array-Oriented Languages and Polyhedral Compilation

Justin Paston-Cooper

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Tags: Computer science, High-level Languages, nVidia, nVidia GeForce RTX 2080, Thesis

September 6, 2020 by hgpu

A Distributed Architecture for Smart Recycling Using Machine Learning

Dimitris Ziouzios, Dimitris Tsiktsiris, Nikolaos Baras, Minas Dasygenis

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Tags: Algorithms, Cloud, Computer science, Computer vision, CUDA, Machine learning, nVidia, Security

September 6, 2020 by hgpu

Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer

Suyoung Lee, HyungSeok Han, Sang Kil Cha, Sooel Son

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Tags: Computer science, Java, Neural networks, nVidia, nVidia GeForce GTX Titan XP, Package, Python, PyTorch, Security

August 30, 2020 by hgpu

Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia

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Tags: Computer science, CUDA, Deep learning, FPGA, GPU cluster, Heterogeneous systems, nVidia, Optimization, Task scheduling, Tesla K80, Tesla P100, Tesla V100

August 30, 2020 by hgpu

CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM

Twinkle Jain, Gene Cooperman

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Tags: Computer science, CUDA, HPC, nVidia, nVidia Quadro K600, Package, Tesla V100

August 30, 2020 by hgpu

8 Steps to 3.7 TFLOP/s on NVIDIA V100 GPU: Roofline Analysis and Other Tricks

Charlene Yang

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Tags: Computer science, HPC, nVidia, OpenACC, Optimization, Package, Performance, Tesla V100

August 30, 2020 by hgpu

Compiler-Based Tools to Aid in Data Transfer Optimization and On-Chip Debug of Heterogeneous Compute Systems

Matthew B. Ashcraft

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Tags: Compilers, Computer science, FPGA, Heterogeneous systems, HLS, nVidia, nVidia GeForce GTX Titan X, OpenCL, Thesis

August 23, 2020 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

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

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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