Cpen 221 fall 2016 introduction to concurrent and

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CPEN 221 – Fall 2016
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Introduction to Concurrent and Parallel Programming 15 Figure 6: Dataflow. program at different times. Instead, it has primitives for parallelism that involve apply- ing the same operation to different pieces of data at the same time. For example, you would have a primitive for applying some function to every element of an array. The implementation of this primitive would use parallelism rather than a sequential for- loop. Hence all the parallelism is done for you provided you can express your program using the available primitives. Examples include vector instructions on some proces- sors and map-reduce style distributed systems. Here is a visual interpretation of data parallelism: 4 Summary In this reading, we have discussed concurrency and parallelism, and provided some intuition for why they matter. We then discussed a model for concurrent programming using shared memory and multithreaded programs . We then illustrated some elementary Java constructs for implementing multithreaded concurrency. Lastly, we touched upon other models for concurrent programming: message passing, dataflow, and data parallelism. CPEN 221 – Fall 2016
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Introduction to Concurrent and Parallel Programming 16 Figure 7: Data parallelism. CPEN 221 – Fall 2016
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  • Fall '17
  • satish
  • Central processing unit, shared memory, Parallel algorithm

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