TL;DR
FitFloat is presented, a drop-in floating-point array replacement supporting user-specified precision on GPUs with the goal of reducing storage requirements of scientific applications while maximizing performance over Unified Memory.
Valk, a performance analysis tool that combines data from multiple profilers, shows that when memory bandwidth is increased, kernels become compute bound, and makes three recommendations to fully utilize the GPUs' potential for relational workloads when the memory wall is removed.
This paper presents an OpenMP-style parallelism API for Uxntal, the stack-based assembly-style language for the Uxn platform, and demonstrates that exemplar code using this API can run at comparable performance even on an integrated GPU.
Experiments show that RT-CDF outperforms the baselines over a broad set of conditions for small to medium ranges, achieving a maximum speedup of 4.39 over the fastest baseline, showing that the cost of histogram construction limits the applicability of RT-CDF to larger ranges.
This study developed mpc\_cuda, an arbitrary-precision arithmetic environment for GPUs, by leveraging generative AI and rerunnable conversion scripts to faithfully port the mini-GMP-based MPFR/MPC libraries to CUDA kernels while reserving a per-thread temporary storage region (a bump arena).
Building on NCCL's device-side API, low-latency interfaces for constructing custom collective kernels are developed and used to implement new symmetric collectives in NCCL, demonstrating benefits for both AI inference and traditional HPC workloads.
This work proposes SAI, a mechanism that virtualizes shared memory into the L2 cache to improve GPU performance for AI applications and introduces an L2 cache management strategy that integrates associativity-based virtual page allocation and a replacement information table, reducing page-swapping overhead while preser...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
