Hastega: Challenge for GPGPU on Elixir @ Lonestar ElixirConf 2019

Hastega: Challenge for GPGPU on Elixir @ Lonestar ElixirConf 2019

We've succeeded in implementing a demonstration program in which an Elixir code invokes directly a GPGPU benchmark by Rustler. We propose a Hastega (Hyper Accelerator of Spreading Tasks for Elixir with GPU Activation) method that converts an Elixir code using Enum/Flow to an executable code for GPU or multi-core CPU with SIMD.
We've conducted a performance evaluation using the Logistic Maps of the experimental implementation of GPGPU using the Hastega method. We've got the following results:
our Hastega is 4-8 times faster than pure Elixir executed by only CPU
our Hastega is up to 3 times faster than CuPy/Python executed with GPU
our Hastega is only 1.5 times slower than native code executed with GPU
Now, we implement Linear Regression and Neural Network in Elixir, and will accelerate it with Hastega. Our main future work is to implement a compiler from Elixir code with Enum/Flow to native code for GPU and/or multi-core CPU with SIMD.

# Bio

Susumu Yamazaki (ZACKY) is currently an Associate Professor at the University of Kitakyushu. His current research projects focus on programming language processors, software engineering, programming education and social implementation of software systems.



Susumu Yamazaki

March 02, 2019