作者: 不透明性 時間: 2025-3-26 18:34
https://doi.org/10.1007/978-3-031-15112-5 thus limiting scalability. Semantic relaxation has the potential to address this issue, increasing the parallelism at the expense of weakened semantics. Although prior research has shown that improved performance can be attained by relaxing concurrent data structure semantics, there is no one-size-
作者: 羽毛長成 時間: 2025-3-26 21:12
作者: cortex 時間: 2025-3-27 01:48
https://doi.org/10.1007/978-3-662-53313-0ave leveraged task graph parallelism to accelerate simulation on a CPU- and/or GPU-parallel architecture. Despite the improved performance, they all assume atomic execution per task and do not anticipate multitasking that can bring significant performance advantages. As a result, we introduce TaroRT
作者: ABYSS 時間: 2025-3-27 09:05
Modeling and Control in Solid Mechanicsuch a problem is the Full Waveform Inversion (FWI), used in several geophysical applications like oil reservoir discovery. Central to solving FWI is Reverse Time Migration (RTM), a Geophysical algorithm for high-resolution subsurface imaging from seismic data that poses considerable computational ch
作者: 珍奇 時間: 2025-3-27 10:09
作者: 諂媚于性 時間: 2025-3-27 16:28
作者: indemnify 時間: 2025-3-27 18:10
作者: REIGN 時間: 2025-3-28 00:38
作者: 玩笑 時間: 2025-3-28 03:54
作者: 神圣在玷污 時間: 2025-3-28 09:43
作者: 教育學 時間: 2025-3-28 14:26
978-3-031-69582-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
作者: 領帶 時間: 2025-3-28 14:38
作者: Glaci冰 時間: 2025-3-28 21:17
Accelerated Block-Sparsity-Aware Matrix Reordering for?Leveraging Tensor Cores in?Sparse Matrix-Mult performance for SpMM is challenging due to the irregular distribution of non-zero elements and memory access patterns. Therefore, several sparse matrix reordering algorithms have been developed to improve data locality for SpMM. However, existing approaches for reordering sparse matrix have not con
作者: CYT 時間: 2025-3-29 02:30
Reduced-Precision and?Reduced-Exponent Formats for?Accelerating Adaptive Precision Sparse Matrix–Vecr adaptive precision algorithms dynamically adapt at runtime the precisions?used for different variables or operations. For example Graillat et al. (2023)?have proposed an adaptive precision sparse matrix–vector product (SpMV)?which stores the matrix elements in a precision inversely proportional to
作者: graphy 時間: 2025-3-29 04:27
Mixed Precision Randomized Low-Rank Approximation with?GPU Tensor Coresstigate the design and development of such methods capable of exploiting recent mixed precision accelerators like GPUs equipped with tensor core units. We combine three new ideas to exploit mixed precision arithmetic in randomized LRA. The first is to perform the matrix multiplication with mixed pre
作者: 親屬 時間: 2025-3-29 08:35
作者: Glossy 時間: 2025-3-29 13:12
Minimizing I/O in?Toom-Cook Algorithmsteger multiplication algorithms frequently used in many applications, particularly for small . sizes (2, 3, and 4). Previous studies focus on minimizing Toom-Cook’s arithmetic cost, sometimes at the expense of asymptotically higher communication costs and memory footprint. For many high-performance
作者: hypertension 時間: 2025-3-29 18:36
GPU-Accelerated BFS for?Dynamic Networkshe electronic design automation (EDA) field to social network analysis. Many contemporary real-world networks are dynamic and evolve rapidly over time. In such cases, recomputing the BFS from scratch after each graph modification becomes impractical. While parallel solutions, particularly for GPUs,
作者: 火光在搖曳 時間: 2025-3-29 21:28
QClique: Optimizing Performance and?Accuracy in?Maximum Weighted Cliquet search-based MWC algorithms and show that high-accuracy weighted cliques can be discovered in the early stages of the execution if searching the combinatorial space is performed systematically. Based on this observation, we introduce QClique as an approximate MWC algorithm that processes the searc
作者: xanthelasma 時間: 2025-3-30 01:03
A Fast Wait-Free Solution to?Read-Reclaim Races in?Reference Counting major programming languages (e.g., Arc in Rust, shared_ptr and atomic
作者: Project 時間: 2025-3-30 05:57
How to?Relax Instantly: Elastic Relaxation of?Concurrent Data Structures thus limiting scalability. Semantic relaxation has the potential to address this issue, increasing the parallelism at the expense of weakened semantics. Although prior research has shown that improved performance can be attained by relaxing concurrent data structure semantics, there is no one-size-
作者: calamity 時間: 2025-3-30 09:54
ALZI: An Improved Parallel Algorithm for?Finding Connected Components in?Large Graphs efficient sequential algorithms for finding connected components in a graph. However, a sequential algorithm can take a long time for a large graph. Parallel algorithms can significantly speed up computation using multiple processors. This paper presents a fast shared-memory parallel algorithm name
作者: 新奇 時間: 2025-3-30 16:04
作者: propose 時間: 2025-3-30 20:35
作者: Gobble 時間: 2025-3-31 00:43
Accelerating Large-Scale Sparse LU Factorization for?RF Circuit Simulation large-scale circuits. Radio frequency (RF) circuits have been increasingly emphasized with the evolution of ubiquitous wireless communication (i.e., 5G and WiFi). The RF simulation matrices show a distinctive pattern of structured dense blocks, and this pattern has been inadvertently overlooked by
作者: 同步左右 時間: 2025-3-31 02:31
作者: Insul島 時間: 2025-3-31 08:49
作者: 消瘦 時間: 2025-3-31 10:53
作者: 謊言 時間: 2025-3-31 16:57
Code Generation for?Octree-Based Multigrid Solvers with?Fused Higher-Order Interpolation and?Communirately capturing local features within a domain while leveraging the efficiency inherent in multigrid techniques. We outline the essential steps involved in generating specialized kernels for local refinement and communication routines which integrate on-the-fly interpolations to seamlessly transfer
作者: Inveterate 時間: 2025-3-31 19:33
作者: Tortuous 時間: 2025-3-31 22:04
作者: canvass 時間: 2025-4-1 03:54
https://doi.org/10.1007/978-3-642-03196-0 memory, avoiding unnecessary disk operations and reducing data transfer time. We conducted extensive experiments on several benchmarks and demonstrated that our GPU cache system can achieve significant speedups compared to the baseline COMPSs implementation.
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