high performance computing on graphics processing units: hgpu.org

Zachary DeVito, Niels Joubert, Francisco Palacios, Stephen Oakley, Montserrat Medina, Mike Barrientos, Erich Elsen, Frank Ham, Alex Aiken, Karthik Duraisamy, Eric Darve, Juan Alonso, Pat Hanrahan

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Package, Partial differential equations, PDEs, Performance, Programming Languages, Tesla C2050

October 14, 2011 by hgpu

Towards scalar synchronization in SIMT architectures

Arun Ramamurthy

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenCL, Performance, Programming techniques, Thesis

October 14, 2011 by hgpu

Firepile: Run-time Compilation for GPUs in Scala

Nathaniel Nystrom, Derek White, Kishen Das

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Tags: Code generation, Compilers, Computer science, nVidia, nVidia GeForce 9800 GT, OpenCL, Package, Performance

October 13, 2011 by hgpu

Input Sensitivity of GPU Program Optimizations

Yixun Liu, Eddy Z. Zhang, Poornima Bhamidipati, Xipeng Shen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, OpenCL, Optimization, Performance, Tesla C1060

October 13, 2011 by hgpu

High Performance Computing with Accelerators

K.A. Akshita

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Tags: Cell processor, Computer science, FPGA, nVidia, Performance, Review

October 12, 2011 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Data Layout Pruning on GPU

Skeleton Programming for Heterogeneous GPU-based Systems

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers

Dymaxion: Optimizing Memory Access Patterns for Heterogeneous Systems

GPU fluids in production: a compiler approach to parallelism

Accelerating code on multi-cores with FastFlow

CudaDMA: Optimizing GPU Memory Bandwidth via Warp Specialization

Liszt: A Domain Specific Language for Building Portable Mesh-based PDE Solvers

Towards scalar synchronization in SIMT architectures

Firepile: Run-time Compilation for GPUs in Scala

Input Sensitivity of GPU Program Optimizations

High Performance Computing with Accelerators

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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