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dss:laboratoare:05 [2019/06/26 15:31] eduard.staniloiu [Exercises] |
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| ==== Exercises ==== | ==== Exercises ==== | ||
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| + | The lab can be found at this [[https://github.com/RazvanN7/D-Summer-School/tree/master/lab-05|link]]. | ||
| === 1. Parallel programming === | === 1. Parallel programming === | ||
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| </note> | </note> | ||
| + | === 4. I did it My way === | ||
| + | |||
| + | Let's implement our own concurrent **map** function. | ||
| + | Navigate to the 4-concurrent-map directory. Starting from the serial implementation found in **mymap.d** modify the code such that | ||
| + | the call to **mymap** function will execute on multiple threads. You are required to use the **std.concurrency** module for this task. | ||
| + | |||
| + | Creating a thread implies some overhead, thus we don't want to create a thread for each element, but rather have a thread process chunks of elements; basically we need a **workUnitSize**. | ||
| + | |||
| + | === 5. Don't stop me now === | ||
| + | |||
| + | Since we just got started, let's implement our our concurrent **reduce** function. **reduce** must take the initial accumulator value as it's first parameter, and then the list of elements to reduce. | ||
| + | |||
| + | <note> | ||
| + | Be careful about those race conditions. | ||
| + | </note> | ||
| + | |||
| + | === 6. Under pressure === | ||
| + | |||
| + | The implementations we did at ex. 4 and ex. 5 are great and all, but they have the following shortcoming: they will each spawn a number of threads (most likely equal to the number of physical cores), so calling them both in parallel will spawn twice the amount of threads that can run in parallel. | ||
| + | |||
| + | Change your implementations to use a thread pool. The worker threads will consume jobs from a queue. The map and reduce implementations will push job abstractions into the queue. | ||
| + | |||
| + | Now we're talking! | ||
