Advanced Parallel Processing Technologies: 10th by Foivos S. Zakkak, Dimitrios Chasapis (auth.), Chenggang Wu, PDF

By Foivos S. Zakkak, Dimitrios Chasapis (auth.), Chenggang Wu, Albert Cohen (eds.)

This booklet constitutes the refereed post-proceedings of the tenth overseas Symposium on complex Parallel Processing applied sciences, APPT 2013, held in Stockholm, Sweden, in August 2013. The 30 revised complete papers provided have been rigorously reviewed and chosen from sixty two submissions. The papers disguise a variety of themes shooting the various state-of-the-art and perform in parallel structure, parallel software program, concurrent and disbursed platforms, and cloud computing, with a spotlight on computing platforms for large info applications.

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Extra info for Advanced Parallel Processing Technologies: 10th International Symposium, APPT 2013, Stockholm, Sweden, August 27-28, 2013, Revised Selected Papers

Example text

42 Y. Cheng et al. (iii) Migrating VM’s memory The user-level scheduler also provides the function of migrating memory to a specified NUMA node using the move pages() system call. Our NUMA-aware VM scheduler adopts two memory migration strategies to migrate a VM’s proper memory pages to its BNN node. The two memory migration strategies are as follows: (1) If a VM’s BNN node is changed to another NUMA node and the VM’s VCPUs are scheduled onto its new BNN node. We then use the Intel PEBS (Precise Event-Based Sampling) functionality [2] of sampling memory instructions to get the memory address of the VM.

Multisort recursively splits an array until a certain threshold, which we set as the BDDT block size. BDDT: Block-Level Dynamic Dependence Analysis for Task-Based Parallelism  
    
                                                             (d) Multisort                
      (c) Jacobi     
             (b) FFT  
     (a) Cholesky          
   27                  (e) Intruder           (f) Ferret Fig.

While data blocks are allocated starting from one end of the slab, pointers to the metadata for these blocks are allocated starting from the opposite end of the slab. Thus, there may be fragmented (unusable) memory in the slab, the amount of which is bounded by the block size. All shared memory and metadata is bulk-deallocated upon completion of all tasks. As such, BDDT does not need special handling for fragmentation. g. Cilk++ and Intel TBB. This is not a limitation of the usability of the programming model.

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