Parallel Computing in the Julia Language
Most modern computers possess more than one CPU, and several computers can be combined together in a cluster. Harnessing the power of these multiple CPUs allows many computations to be completed more quickly. There are two major factors that influence performance: the speed of the CPUs themselves, and the speed of their access to memory. In a cluster, it’s fairly obvious that a given CPU will have fastest access to the RAM within the same computer (node). Perhaps more surprisingly, similar issues are very relevant on a typical multicore laptop, due to differences in the speed of main memory and the cache. Consequently, a good multiprocessing environment should allow control over the “ownership†of a chunk of memory by a particular CPU. Julia provides a multiprocessing environment based on message passing to allow programs to run on multiple processes in separate memory domains at once.
Recent comments
20 weeks 1 day ago
20 weeks 1 day ago
20 weeks 1 day ago
42 weeks 2 days ago
46 weeks 4 days ago
48 weeks 1 day ago
48 weeks 1 day ago
50 weeks 6 days ago
1 year 3 weeks ago
1 year 3 weeks ago