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Titlebook: Euro-Par 2019: Parallel Processing; 25th International C Ramin Yahyapour Conference proceedings 2019 Springer Nature Switzerland AG 2019 ar

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樓主: Concave
11#
發(fā)表于 2025-3-23 13:17:49 | 只看該作者
Toggle: Contention-Aware Task Scheduler for Concurrent Hierarchical Operationss. State-of-the-art approaches for hierarchical locking are unaware of how tasks are scheduled. We propose a lock-contention aware task scheduler which considers the locking request while assigning tasks to threads. We present the design and implementation of ., which exploits nested intervals and w
12#
發(fā)表于 2025-3-23 15:40:23 | 只看該作者
13#
發(fā)表于 2025-3-23 21:31:11 | 只看該作者
14#
發(fā)表于 2025-3-24 00:35:16 | 只看該作者
15#
發(fā)表于 2025-3-24 02:53:53 | 只看該作者
PLB-HAC: Dynamic Load-Balancing for Heterogeneous Accelerator Clustersmputational load among them. Their relative processing speed for each target application is not available in advance and must be computed at runtime. Also, dynamic changes in the environment may cause these processing speeds to change during execution. We propose a Profile-based Load-Balancing algor
16#
發(fā)表于 2025-3-24 09:31:51 | 只看該作者
17#
發(fā)表于 2025-3-24 11:35:01 | 只看該作者
A Comparison of Random Task Graph Generation Methods for Scheduling Problemsamong a set populated mainly with trivial ones, we rely on properties such as the ., which measures how much a task graph can be decomposed into smaller ones. This property and an in-depth analysis of existing random instance generators establish the sub-exponential generic time complexity of the studied problem.
18#
發(fā)表于 2025-3-24 15:01:11 | 只看該作者
https://doi.org/10.1007/978-3-322-90289-4ork-stealing to maximize throughput. Using widely used STMBench7 benchmark, a real-world XML hierarchy, and a state-of-the-art hierarchical locking protocol, we illustrate that . considerably improves the overall application throughput.
19#
發(fā)表于 2025-3-24 22:42:33 | 只看該作者
20#
發(fā)表于 2025-3-24 23:48:22 | 只看該作者
Rhinomanometrische Untersuchungenffected source-code locations from instrumentation, allowing the profiler to skip them at runtime and avoiding the associated overhead. At the end, we merge static and dynamic dependences. We evaluated our approach with 38 benchmarks from two benchmark suites and obtained a median reduction of the profiling time by 62% across all the benchmarks.
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