Month: November 2021

TLDR:

1. Use C libraries (via NIF) for matrix computation when performance is the top priority. Otherwise, it is about $10^3$ times slower in terms of matrix computation.
2. OTP 25 introduces JIT on ARM64 and it shows 3-4% performance improvement (matrix computation).
3. Almost linear speedup can be achieved when a large computation task can be divided into independent smaller ones.
4. Apple M1 Max performs much better than its x86_64 competitors (Intel Core i9 8950HK and AMD Ryzen 9 3900XT).

Benchmark code here: https://github.com/cocoa-xu/CIFAR-10-livebook

Numerical Elixir

I started to use Elixir/Erlang about 2 months ago, and I learned the existence of Numerical Elixir (Nx) from my supervisor, Lito.

Basically, Nx to Elixir is like NumPy to Python. They implemented a number of numerical operations, especially for multi-dimensional arrays. It's worth noting that Nx comes with a built-in auto-grader, which means that we don't have to write the corresponding differentiate functions for backwards-propagating when training a neural network.

I explored the Nx and tried to write some benchmarks to evaluate its performance with different hardware (Raspberry Pi 4, x86_64 laptops and desktops, ARM64 laptops) and conditions (Allow calls to external C libraries vs. Pure Elixir implementation). And here I finally got some numbers!

P.S. The goal of this benchmark is only to evaluate the matrix computation performance, instead of getting a decent (or even acceptable) CIFAR-10 prediction accuracy.

Benchmark Settings

Hardware

• Raspberry Pi 4, 8 GB of RAM. Ubuntu 20.04 aarch64.
• x86_64 laptop. Intel 8th Gen Core i9 8950HK, 6 Cores 12 Threads, MacBook Pro (15-inch, 2018), 32 GB RAM. macOS Big Sur 11.1 x86_64.
• x86_64 desktop. AMD Ryzen 9 3900XT, 12 Cores 24 Threads, Desktop PC, 64 GB RAM, NVIDIA RTX 3090. Ubuntu 20.04 x86_64.
• ARM64 laptop. M1 Max, 10 Cores (8 Performance + 2 Effiency) 10 Threads, MacBook Pro (14-inch, 2021), 64 GB RAM. macOS Montery 12.0.1 aarch64.

Software

• Erlang OTP 24.0.6 and 25@b58c66e12.
• Numerical Elixir, Nx@e90de80157.
• LibTorch CPU, v1.9.1.
• LibTorch GPU, v1.9.1. CUDA 11.1, cuDNN 8.2.1.

Dataset

CIFAR-10 binary version.

Method

• 3-layer DenseNN.
  1. Input layer. Dense layer, size {nil, 1024, 64} + {nil, 64}, activation sigmoid.
  2. Hidden layer. Dense layer, size {nil, 64, 32} + {nil, 32}, activation sigmoid.
  3. Output layer. Dense layer, size {nil, 32, 10} + {nil, 10}, activation softmax.

• Number of epochs: 5.
• Batch size.
  • 300 when using Nx.BinaryBackend, single-thread
  • 250 * n_jobs when using Nx.BinaryBackend, multi-thread. n_jobs will be the number of available logical cores.
  • 300 when using Torchx.Backend.

• Binary.

Benchmark.run(
  backend: Nx.BinaryBackend,
  batch_size: 300,
  n_jobs: 1
)

• Binary MT.

Benchmark.run(
  backend: Nx.BinaryBackend,
  batch_size:250 * System.schedulers_online(),
  n_jobs: System.schedulers_online()
)

• Torch CPU/GPU.

Benchmark.run(backend: Torchx.Backend, batch_size: 300)

Benchmark Results

Numbers are in seconds.

I'll fill in the empty cells when the rest benchmarks are done.