WebJun 18, 2014 · In this work, we present a software (compiler) technique named Collaborative Context Collection (CCC) that increases the warp execution efficiency when faced with thread divergence incurred either by different intra-warp task assignment or by intra-warp load imbalance. WebNov 12, 2015 · 1.1.1 Thread divergence. GPUs implement the “single instruction multiple threads (SIMT)” architecture. Threads are organized into SIMT units called warps, and the warp size in CUDA is 32 threads. Threads in the same warp start executing at the same program address but have private register state and program counters, so they are free …

On-GPU thread-data remapping for nested branch divergence

WebFigure 1: Operand Values–Baseline GPU and Affine Computation Figure 1 shows how affine computations can be computed much more efficiently than their direct SIMT … Webow divergence can result in signi cant performance (compute throughput) loss. The loss of compute through-put due to such diminished SIMD e ciency, i.e., the ratio of enabled to available lanes, is called the SIMD divergence problem or simply compute divergence. We also classify ap-plications that exhibit a signi cant level of such behavior as high fen farm methwold

Effective SIMD efficiency for conventional SIMT.

WebSIMT efficiency and thereby hurts overall execution time [6]. We propose a code motion optimization, called Common Subexpression Con-vergence (CSC), that helps reduce the … WebFeb 22, 2024 · The global scheduler of a current GPU distributes thread blocks to symmetric multiprocessors (SM), which schedule threads for execution with the … WebMots-clés : GPU, SIMT, divergence, microarchitecture 1. Introduction Graphics Processing Units (GPUs) execute multi-thread programs (kernels) on SIMD units by grouping threads running in lockstep into so-called warps. This model is called SIMT (Single Instruction Multiple Threads) [7]. As the multi-thread programming model allows branching, high fence storage units