Towards a Benchmarking Suite for Kernel Tuners

As computing system become more complex, it is becoming harder for programmers to keep their codes optimized as the hardware gets updated. Autotuners try to alleviate this by hiding as many archite…

As computing system become more complex, it is becoming harder for programmers to keep their codes optimized as the hardware gets updated. Autotuners try to alleviate this by hiding as many archite…


Benchmark your UX by first determining appropriate metrics and a study methodology. Then track these metrics across different releases of your product by running studies that follow the same established methodology.

12 sentiment analysis algorithms were compared on the accuracy of tweet classification. The fasText deep learning system was the winner.
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We’re releasing an analysis showing that since 2012 the amount of compute needed to train a neural net to the same performance on ImageNet classification has been decreasing by a factor of 2 every 16 months. Compared to 2012, it now takes 44 times less compute to train a neural…

I’m not going to bury the lede: Most machine learning benchmarks are bad. And not just kinda-sorta nit-picky bad, but catastrophically and fundamentally flawed. TL;DR: Please, for the love of sta…

MLCommons ML benchmarks help balance the benefits and risks of AI through quantitative tools that guide responsible AI development.

MLCommons ML benchmarks help balance the benefits and risks of AI through quantitative tools that guide responsible AI development.

Over the last few years we have detailed the explosion in new machine learning systems with the influx of novel architectures from deep learning chip

Which machine learning algorithm should you use? It is a central question in applied machine learning. In a recent paper by Randal Olson and others, they attempt to answer it and give you a guide for algorithms and parameters to try on your problem first, before spot checking a…