Quinn, Michael J. 1994.Parallel Computing: Theory and Practice, 2nd ed. McGraw-Hill. Sengupta, Shubhabrata, Aaron E. Lefohn, and John D. Owens. 2006. "A Work-Efficient Step-Efficient Prefix Sum Algorithm." InProceedings of the Workshop on Edge Computing Using New Commodity Architecture...
1992S. Chaudhuri and J. Radhakrishnan, The complexity of parallel prefix problems on small domains, in Proc. 33rd IEEE Symp. on Found. of Comput. Sci., Pittsburgh, PA, IEEE Computer Society Technical Committee on Mathematical Foundations of Computing, 1992, pp. 638-647....
technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. ...
Notice that computing each of these paths requires 1ipt. However, it is easy to see that with the alternative partitioningV1={1,3,6},V2={2,4,5}, only paths (3, 2), (5, 4) require traversing a partition boundary, although this partitioning is not optimal with respect to min edge-...
OpenMP library is the C/C++ parallel computing library. Most Linux releases have OpenMP already been installed in the system. In Mac OS X, to install the compiler that supports OpenMP, we recommend using the Homebrew package manager: brew install gcc Rscript environment For statistical analysis...
using an index. With a parallel plan, the delay is shortened because more resource is used. The parallel plan could use up toDtimes more resource, whereDis the degree of parallelism. A value between 0 and 100 sets an intermediate trade-off between throughput and response time. Low values ...
Other example functions can be run on the maps, such as computing a subsequence of the map: // Compute prefixes and suffixes auto prefix = integer_map::subseq(m1, 0, (2 * m1.size()) / 3); auto suffix = integer_map::subseq(m1, (1 * m1.size()) / 3, m1.size()); std::cou...
OpenMP is a powerful programming interface that simplifies parallel computing, allowing you to tap into the full potential of your hardware. By effectively parallelizing your applications, you can achieve significant performance gains, saving valuable time and resources. ...
How to define 'local' matlabpool size at... Learn more about parallel computing, matlabpool, compiler, local configuration
When computing how many jobs to run simultaneously relative to the number of CPU cores you can ask GNU parallel to instead look at the number of physical CPUs. This will make sense for computers that have hyperthreading as two jobs running on one CPU with hyperthreading will run slower than...