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GPUHarbor: Testing GPU Memory Consistency at Large

Reese Levine, Mingun Cho, Devon McKee, Andrew Quinn, Tyler Sorensen

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Tags: AMD Radeon Pro 5500M, ATI, Computer science, Memory model, nVidia, nVidia GeForce RTX 3080, nVidia Tegra TX1, OpenCL, Performance

June 11, 2023 by hgpu

XaaS containers

Acceleration as a Service (XaaS) Source Containers

microSYCL: SYCL micro-benchmarks repository

Exploring SYCL as a Portability Layer for High-Performance Computing on CPUs

SYCL Container

Exploring SYCL for batched kernels with memory allocations

CASS: Cuda-Amd aSSembly

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

Cluser of smartphones for edge computing application using TensorFlow

Low-cost edge computing using upcycled smartphones

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

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