By Xian-he Sun, Wenyu Qu, Ivan Stojmenovic, Wanlei Zhou, Zhiyang Li, Hua Guo, Geyong Min, Tingting Yang, Yulei Wu, Lei Liu (eds.)
This quantity set LNCS 8630 and 8631 constitutes the court cases of the 14th overseas convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2014, held in Dalian, China, in August 2014. The 70 revised papers awarded within the volumes have been chosen from 285 submissions. the 1st quantity includes chosen papers of the most convention and papers of the first foreign Workshop on rising issues in instant and cellular Computing, ETWMC 2014, the fifth foreign Workshop on clever communique Networks, IntelNet 2014, and the fifth foreign Workshop on instant Networks and Multimedia, WNM 2014. the second one quantity includes chosen papers of the most convention and papers of the Workshop on Computing, verbal exchange and keep an eye on applied sciences in clever Transportation process, 3C in ITS 2014, and the Workshop on safety and privateness in machine and community structures, SPCNS 2014.
Read or Download Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part I PDF
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Additional resources for Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part I
1, the Inter-Process Communication (IPC) feature has been introduced to facilitate direct data copy among multiple GPU buﬀers that are allocated by diﬀerent processes. The IPC is implemented by creating and exchanging memory handles among processes and obtaining the device buﬀer pointers of others. This feature has been utilized in CUDA-aware MPI libraries to optimize communications within a node. Therefore, we decided to implement the communication among multiple GPUs by calling the low-level IPC functions and asynchronous CUDA memory copy functions directly, instead of using high-level CUDA-aware MPI functions.
In the east/west outer part, the width is set to 32 to ensure coalesced memory access in a warp to improve performance. The halo part is also allocated to stream 2. The workﬂow of multiple streams on the GPU is shown in Fig. 3. The outer parts are normal kernel functions that can run in parallel with the inner part through diﬀerent streams. The communication operations are implemented by cudaM emcpyAsync, which will be detailed later. The corresponding synchronization operation between the CPU and the GPU or among MPI processes are implemented with cudaStreamSynchronize function and M P I barrier function.
Due to the deployment mode and characteristics of Web services, the QoS inforamtion of Web services is greatly inﬂuenced by the service user’s location and the invocation time. In reality, the QoS data of Web services usually is sparse. Also, the QoS Value of Web services may change over time due to the dynamic environment and how to handle the dynamic data streams of incoming service QoS value is a big challenge. All these problems should be considered in Web service recommendation. On the analysis of the existing problems, we propose a Web Service Recommendation Framework by way of exploiting temporal QoS information.