冒烟
发表于 2025-3-23 13:17:49
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
Barrister
发表于 2025-3-23 15:40:23
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FIS
发表于 2025-3-23 21:31:11
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Suppository
发表于 2025-3-24 00:35:16
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reflection
发表于 2025-3-24 02:53:53
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
专横
发表于 2025-3-24 09:31:51
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anesthesia
发表于 2025-3-24 11:35:01
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.
博爱家
发表于 2025-3-24 15:01:11
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.
圆木可阻碍
发表于 2025-3-24 22:42:33
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佛刊
发表于 2025-3-24 23:48:22
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.