The paper focuses on evaluating the efficiency of different implementations of ridge regression for brain encoding using a large-scale fMRI dataset (CNeuroMod Friends dataset). The key highlights are:
Brain encoding models successfully captured brain activity in the visual cortex, with moderate correlation between predicted and real fMRI time series.
Multithreaded execution with the Intel MKL library significantly outperformed the OpenBLAS library, providing a 1.9x speedup using 32 threads on a single machine.
The performance benefits of multi-threading were limited and reached a plateau after 8 threads.
The scikit-learn MultiOutput parallelization was found to be impractical, being slower than multi-threading on a single machine due to redundant computations.
The authors proposed a new "Batch-MultiOutput" approach, which partitions the brain targets into batches and processes them in parallel across multiple machines, with multi-threading applied concurrently within each batch.
The Batch-MultiOutput regression scaled well across compute nodes and threads, providing speed-ups of up to 33x with 8 compute nodes and 32 threads compared to a single-threaded scikit-learn execution.
The conclusions likely apply to many other applications featuring ridge regression with a large number of targets.
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Önemli Bilgiler Şuradan Elde Edildi
by Sana Ahmadi,... : arxiv.org 03-29-2024
https://arxiv.org/pdf/2403.19421.pdfDaha Derin Sorular