![]() ![]() "Superlinear Speedup for Parallel Backtracking". Parallel Computational Fluid Dynamics 2007: Implementations and Experiences on Large Scale and Grid Computing. "Parallel Three Dimensional Direct Simulation Monte Carlo for Simulating Micro Flows". Microprocessor Architecture: From Simple Pipelines to Chip Multiprocessors. Computer Architecture: A Quantitive Approach. ![]() Super-linear speedups can also occur in parallel implementations of branch-and-bound for optimization: the processing of one node by one processor may affect the work other processors need to do for the other nodes. Super-linear speedups can also occur when performing backtracking in parallel: an exception in one thread can cause several other threads to backtrack early, before they reach the exception themselves. There the accumulated RAM from each of the nodes in a cluster enables the dataset to move from disk into RAM thereby drastically reducing the time required by e.g. ![]() Īn analogous situation occurs when searching large datasets, such as the genomic data searched by BLAST implementations. With the larger accumulated cache size, more or even all of the working set can fit into caches and the memory access time reduces dramatically, which causes the extra speedup in addition to that from the actual computation. One possible reason for super-linear speedup in low-level computations is the cache effect resulting from the different memory hierarchies of a modern computer: in parallel computing, not only do the numbers of processors change, but so does the size of accumulated caches from different processors. Super-linear speedup rarely happens and often confuses beginners, who believe the theoretical maximum speedup should be A when A processors are used. Sometimes a speedup of more than A when using A processors is observed in parallel computing, which is called super-linear speedup. In marketing contexts, speedup curves are more often used, largely because they go up and to the right and thus appear better to the less-informed. there is no need to plot a "perfect speedup" curve.it is easy to see how well the improvement of the system is working.all of the area in the graph is useful (whereas in speedup curves half of the space is wasted).In engineering contexts, efficiency curves are more often used for graphs than speedup curves, since Programs with linear speedup and programs running on a single processor have an efficiency of 1, while many difficult-to-parallelize programs have efficiency such as 1/ln( s) that approaches 0 as the number of processors A = s increases. Latency of an architecture is the reciprocal of the execution speed of a task: Speedup can be defined for two different types of quantities: latency and throughput. However, speedup can be used more generally to show the effect on performance after any resource enhancement. The notion of speedup was established by Amdahl's law, which was particularly focused on parallel processing. More technically, it is the improvement in speed of execution of a task executed on two similar architectures with different resources. In computer architecture, speedup is a number that measures the relative performance of two systems processing the same problem. Look up speedup in Wiktionary, the free dictionary.
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