Tag
The Futhark language blog discusses a proposed `flatmap` SOAC extension to expose irregular arrays to programmers, enabling recursive data-parallel algorithms like quicksort while preserving efficient GPU compilation.
This paper presents DNSSE, a hybrid framework combining LLM-guided schedule prediction, symbolic constraint solving, and coverage-guided stochastic mutation for verifying distributed parallel AI programs. It detects 2.9x more concurrency bugs than the baseline and raises branch coverage from 68.6% to 91.6% on realistic benchmarks.
The Futhark compiler now supports full flattening of nested data parallelism, allowing any Futhark program to be compiled to parallel GPU code, a milestone achieved after years of work by students and researchers.
A blog post exploring parallel programming concepts through the lens of the HipKittens paper, focusing on the eight-wave ping-pong schedule that overlaps computation with memory movement on AMD GPUs, drawing philosophical parallels to Zen principles.
A reflective essay connecting parallel programming concepts like communication and synchronization with Zen philosophy and human internal coordination.
This article examines the declining popularity of Fortran in scientific computing and proposes modernizing it with dependent types to address the shortage of skilled Fortran programmers.
Stanford has released a free course on Parallel Programming, GPU, and CUDA, consisting of 24 hours of content across 19 lessons, covering GPU architecture, CUDA, and performance optimization.