Parallelization Sample Clauses
Parallelization. ▇▇▇▇▇▇▇ offers to possibility to make multiple calls to the same service in one workflow. This parallelization makes workflows with multiple iterations to finish earlier. The first simple test, carried out in UPF about parallelization demonstrated that simply doubling (x2) one web service in a workflow with only that service reduced the execution time in half. Parallelization seems to be a great advantage but it has its drawbacks. Web services are run on machines with limited resources (processors, memory, etc.) which cannot handle infinite parallel calls to their web services. One problem is that most of those limits can only be measured empirically. Some web service providers offer information about the limits of their web services on the Registry. A bad use of parallelization may cause the server to fail or to be very slow which is the opposite of the desired behavior. To avoid this situation WSP and users can both take precautions: WSP can implement a system to limit the amount of parallel requests. On the other hand, users should follow the recommendations found on the documentation of the web service (Usage conditions). Figure 5 shows how to use the parallelization parameter for a web service in Taverna. The documentation about Parallelization can be found on the Taverna tutorial.
Parallelization. The existing 2D simulation code, SuMO, has been analyzed regarding algorithm and data structures. It is clear that datastructures needed to change substantially for the parallelization of the algorithms.
Parallelization. Taverna offers to possibility to make multiple calls to the same service in one workflow. This parallelization makes workflows with multiple iterations to finish earlier. The first simple test, carried out in UPF about parallelization demonstrated that simply doubling (x2) one web service in a workflow with only that service reduced the execution time in half. Parallelization seems to be a great advantage but it has its drawbacks. Web services are run on machines with limited resources (processors, memory, etc.) which cannot handle infinite parallel calls to their web services. One problem is that most of those limits can only be measured empirically. Some web service providers offer information about the limits of their web services on the Registry. A bad use of parallelization may cause the server to fail or to be very slow which is the opposite of the desired behavior. Figure 3 shows how to use the parallelization parameter for a web service in Taverna. The documentation about Parallelization can be found on the Taverna tutorial.
Parallelization. The optimization of the L0 secondary codebooks (of size L1) can be parallelized using a multicore processing platform. Generally the number of core processing units will be less than the number of L0 of codebooks, and the allocation of each optimization process should be done dynamically, since the duration of each optimization process can be different. It is approximated in the example code by the required number of iterations, in order to provide an example of dynamic allocation to the different cores. This partitioning scheme, mostly task-based, is convenient for execution on the ARM subsystem. The load imbalance issue can be easily addressed by dynamically allocating parallel tasks to available processor, rather than statically determining a workload partitioning scheme.