M1 Apple silicon ML performance

Question

Testing out a new M1 Macbook pro with Mathematica 13.0.0. I verified that I used the correct version and it is running natively on ARM.

Mathematica in general runs great, and much fast than on my previous 2015 Macbook (Intel). The benchmarking function (Benchmark[]) confirms this.

Now, when using the ML features of Mathematica this changes. Here is a simple LeNet example from https://reference.wolfram.com/language/tutorial/NeuralNetworksIntroduction.html#621730217

trainingData = ResourceData["MNIST", "TrainingData"];
testData = ResourceData["MNIST", "TestData"];
NetTrain[NetModel["LeNet"], trainingData, All, ValidationSet -> testData]

When I run this I'm getting slightly over 700 samples/second. Here is the NetTrain result object:

For comparison, my 2015 Intel Macbook (i7 4980HQ) manages close to 2000 examples/s on the same example using the CPU.

My question is if this is just the state of Mathematica and the new ARM architecture or is there something that can be done to speed up the ML functions for Apple Silicon based macs?

Thanks everyone, E

Answer 1

It may be an issue where your machine isn't utilizing the parallel kernels. I'm using an M1 Mac and notice that by default it doesn't use all of the cores unless you use LaunchKernels first. If you run the Benchmarking functions you should also notice a speedup if you use LaunchKernels first.

When running your example in a fresh notebook I get a slow speed initially as shown here with around 138 examples/s.

If I first launch parallel kernels I get a significant speedup, around 760 examples/s.

LaunchKernels[];
NetTrain[NetModel["LeNet"],trainingData,All,ValidationSet->testData]