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.
Abstract
We study unstable integer sorting on GPUs for arrays whose elements lie in a known integer range. Focusing on counting-sort-based methods that determine the output interval of each value from its frequency and the prefix sums of the frequencies, we propose and evaluate Range-Tiled CDF sort (RT-CDF), which partitions the possible value range into small intervals, called tiles, that fit in shared memory. For each tile, RT-CDF constructs a histogram, computes its prefix sum as a local CDF, and directly generates the output array from the local CDF. We compare RT-CDF against three baselines: CUB DeviceRadixSort, whose processed bit range is restricted to $[0,\lceil\log_2 R\rceil)$ to exploit the known range size $R$; Ref-H-P sort; and an implementation based on the algorithm of Kolonias et al. Experiments on an NVIDIA GeForce RTX 4090 with range sizes from $R=2^7$ to $2^{18}$, input sizes from $n=10^6$ to $10^9$, and uniformly distributed, normally distributed, and all-equal inputs 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. For $R=2^{18}$, however, at least one baseline outperforms RT-CDF for every evaluated input size and input distribution, showing that the cost of histogram construction limits the applicability of RT-CDF to larger ranges.
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.
The main result concerns the plain-load path: attained LDG bandwidth peaks at a small offered per-thread load (K ~ 2) and then declines, by about 35% from K=2 to K=8 at the authors' primary configuration.
SHARDLP is presented, a distributed GPU LP solver that keeps the matrix and primal-dual state partitioned from sharded input through solution output and reaches the published criterion on nine of eleven instances, on the Google PDLP benchmark.
Arnaud Deza, S. Dey, P. Van Hentenryck· 1 citation
FROG is a GPU-oriented RFANNS index that replaces multiple locally optimal substructure building with a globally aware, vertex-centric design and organizes diverse expansion neighbor candidates for each vertex in a GPU-friendly structure and rapidly identifies the expansion neighbors used for computation at query time.
Xiao-Kun Cui, Peng Liu, Jia-Dong Xie et al.· 0 citations
RVANNS is presented, an RVV-oriented ANNS engine that jointly optimizes vector representation and graph locality and achieves 3.39x and 4.94x speedups over scalar execution on real 128-bit and 256-bit RVV processors, respectively.
Cheng-Ying Huan, Yudong Liu, Jian-Guo Wang et al.· 0 citations
Range reduction plays a crucial role in the accuracy and performance of evaluating trigonometric functions, and is often the primary bottleneck for large floating-point inputs. While fast algorithms such as Cody--Waite work efficiently over narrow intervals, the Payne--Hanek algorithm remains the standard technique for...
Tue Ly· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.