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Merge in existing implementations of the correlations. #1

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danielballan opened this issue Aug 28, 2019 · 2 comments
Open
4 tasks

Merge in existing implementations of the correlations. #1

danielballan opened this issue Aug 28, 2019 · 2 comments

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@danielballan
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We plan to merge in all the existing implementations we can find that are accessible from Python. A requirement for merging a new implementation is that it be installable on our CI service and pass a nominal test. A very minimal smoke test is fine, to start, just something to show that the code runs,

These include variants of 1-time and 2-time (and higher-order if they exist.)

We are open to adding any others that we don't currently know about.

@pierrepaleo
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Hi,

If you are interested, we are also developing a software named dynamix for (one time) correlation function computation. It primarily aims at being simple and fast.
The front-end code is in Python, calling OpenCL/CUDA routines through pyopencl/pycuda.

It features two kinds of correlators : one designed for regular data, one for "sparse" data.
The correlators are unit tested against reference "legacy" implementations, so we are also interested in validating the code on synthetic datasets with analytically known g_2.

The project is still at a early development stage, so we are open to suggestions.

Best regards,

Pierre

@danielballan
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Hi @pierrepaleo. Thanks for reaching out! Your message above must have slipped through my GitHub notifications, and I just now noticed it. That looks very relevant to our interests! I feel we are still in the process of wrapping our minds around what we have and what we need, so it would be great to chat and hear more about what you have and what you need. Would you be interested in having a video chat after the holidays?

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