Wolfram.Research.Mathematica.v9.0.0.0.Keymaker-AGAiN.7z |VERIFIED| 📦

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Wolfram.Research.Mathematica.v9.0.0.0.Keymaker-AGAiN.7z

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Wolfram.Research.Mathematica.v9.0.0.0.Keymaker-AGAiN.7z
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Hyper-threading to speed up a calculation

I’m going to perform statistical simulations with R. One task is to calculate an order statistic for various datasets.
What I do is to store all the datasets in a C++ matrix and calculate a single order statistic value for each dataset using apply. This is done for 1000×1000 subsets of the matrix. I start the simulation with the median matrix and end with a value very close to the expected median (but still 3.000 times bigger) and am calculating the order statistic value for each subset I’m generating for.
My matrix is 1000×1000, runs ~25s in OpenMP, and for each dataset is ~100MB. For a single calculation it’s fine, but if I run 1000 calculations in parallel with 16 threads, each operating on 20 MB of the matrix, I have to run 18h.
My question is, if I use hyper-threading (to reduce the main CPU frequency to 2GHz), will that reduce the calculation time to 10h and would that help? If the calculation time is a two phase process (intial sorting of data and the actual calculation), does hyper-threading affect that in any way?
Please, help me decide if I should use hyper-threading or not.

Hyper-threading is unlikely to help much. It only allows cores to more efficiently share some of their resources (e.g. cache).
The main thing that hyper-threading does is shift the grain of the workload – from 100 MB memory transfers and disk I/O to 30 MB memory transfers and disk I/O. It allows you to activate a thread whenever a core has data to work on. For a small matrix you should find that scaling matters a bit more than trying to speed up memory accesses or disk I/O.

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