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Titlebook: Algorithms and Architectures for Parallel Processing; 23rd International C Zahir Tari,Keqiu Li,Hongyi Wu Conference proceedings 2024 The Ed

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樓主: mountebank
11#
發(fā)表于 2025-3-23 12:58:41 | 只看該作者
Handbibliothek für Bauingenieurering verifiable integrity of query results. Furthermore, we construct an index structure based on B+ tree and Merkle tree to enhance the system’s availability and query efficiency. Our experimental results demonstrate that our scheme not only has excellent performance in terms of the cost of verific
12#
發(fā)表于 2025-3-23 16:21:34 | 只看該作者
13#
發(fā)表于 2025-3-23 21:09:03 | 只看該作者
Die Grundlagen der Festigkeitsberechnung, load balancing mechanism called DRLB. Specifically, DRLB differentiates heterogeneous traffic by using Deep Reinforcement Learning (DRL) in conjunction with the Distributed Distributional Deterministic Policy Gradients (D4PG) algorithm to make the optimal (re)routing for long flows while adopting t
14#
發(fā)表于 2025-3-24 02:06:09 | 只看該作者
Die Grundlagen der Festigkeitsberechnung,n Mininet simulation show that RACO effectively increases the throughput of long flows and reduces the average flow completion time (FCT) of short flows by up to 42% and 61%, respectively, compared with the state-of-the-art load balancing mechanisms.
15#
發(fā)表于 2025-3-24 04:29:51 | 只看該作者
,Modelle von einfachen Werkstücken,e significantly by reducing latency and increasing throughput in stable network conditions. For example, HAECN effectively improves throughput by up to 47%, 34%, 32% and 24% over DCQCN, TIMELY, HPCC and ACC, respectively.
16#
發(fā)表于 2025-3-24 06:37:00 | 只看該作者
,Addressing Coupled Constrained Reinforcement Learning via?Interative Iteration Design,a balancing loss for self-coupled actions, which enables the policy to pursue high task reward while complying with the objective constraints via the balancing feedback from the environment. Additionally, we conceive a notion of coupling compactibility to guide the decoupling of high-dimensional cou
17#
發(fā)表于 2025-3-24 12:51:19 | 只看該作者
18#
發(fā)表于 2025-3-24 18:46:01 | 只看該作者
,Performance Evaluation of?Spark, Ray and?MPI: A Case Study on?Long Read Alignment Algorithm,ented its parallel versions using Ray and MPI, respectively. Furthermore, we selected IMOS as the Spark version of minimap2. The experiments involved six real datasets and one simulated dataset to evaluate and compare speedup, efficiency, throughput, scalability, peak memory, latency, and load balan
19#
發(fā)表于 2025-3-24 21:33:19 | 只看該作者
,Fairness Analysis and?Optimization of?BBR Congestion Control Algorithm,the size of the RTT. The experimental results show that the BBR algorithm prefers long RTT flows. In contrast, the BBR-O algorithm can effectively reduce the goodput difference between flows with different RTT sizes, increasing the values of inflight and sendrate for short RTT flows. The BBR-O algor
20#
發(fā)表于 2025-3-25 02:24:55 | 只看該作者
,Segmenta: Pipelined BFT Consensus with?Slicing Broadcast,on of all replicas, thus reducing the leader’s network overhead. To avoid the increase in communication steps, the additional actions brought by the block shards broadcast are integrated into different consensus phases. Meanwhile, we propose a pipelined version of Segmenta, which further optimizes t
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