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More info: Reading performance

Reading with analysis performance

In addition to the streaming-only task above, this test adds a call to the detector interface to access the data. For this test, the interface was the "hsd" and the call is to access peaks found in different channels. We performed the test for the case of 32 and 60 streams and in the plot shows the comparison with the stream-only task.

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Peak Finding performance

We obtained an example code for running peaking finding algorithm based of the featured extracted data. The experiment used for running this test is different from the one used in the prior tests. This new dataset (exp='tmolv9418',run=175) contains data with peaks, which can be tested with Xiang's peakfinding algorithm (xiangli@slac.stanford.edu). We have around 30,000 events from 15 data streams and ~10,000 events have usable peaks. 

In this test, we investigate the behavior of the algorithms with increasing no. of events. Scaling test is underway when we can obtain more events for running with high no. of nodes. We collected timing spent

The following plot shows weak scaling when both no. of events and no. of cores increased (200k to 25M events and 18 to 2000 cores). 

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Peak Finding and Data Writing

This test shows how data streaming, peaking finding, and data writing performance looks like. Data writing is done by limiting the size of array to 100 for each event and all events get written out (zeroed array in case there's no peak). No. of Srv cores was set to increased the same way as no. of Eb cores.

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