High Performance Machine Learning
Hi there, this is a brief introduction about myself and what I’m working on recently.
My name is Cem. My education life has some zigzagging from engineering to astrophysics, but finally I found myself in the world of scientific computation. Nowadays I’m finishing my Master’s degree on Computational Science and Engineering at Istanbul Technical University.
I enjoy thinking about mathematical modeling of natural and social phenomenons. Being able to define something we see in the nature in terms of mathematics and write it down in a form that computers can understand and solve always fascinates me. I think the best part of the computational studies is that you can focus on any topic you interested in from behavior of the people to universe. The problem which we are concerning about can be as small as Covid-19 virus or as big as a super massive black hole! So no chance to be get bored for computational scientist!
Beside this, I enjoy outdoor activities and amateur astronomy. Whenever I have free time, I throw mysef to mountains and forests where I can see a clear sky above and hear the beautiful sound of the nature.
When I am invited to the PRACE’s SoHPC program, it was such a happy moment for me, because I was sure that I will learn a lot during this program. I joined the team of High Performance Machine Learning project (#2001) with 3 weeks delay. Even though I started the program late, I have already learnt new concepts about HPC such as GASPI.
These days I’m working on implementation of a popular machine learning algorithm, called Gradient Boosting, in C language from scratch. I have used Gradient Boosting via Python’s machine learning module, Scikit-learn, before. With ready-to-use library in hand, it was pretty easy to implement no matter how complex the algorithm. Writing a complex machine learning algorithm by myself is completely different challenge for me.
As the most natural way to start, we are currently focusing on writing a serial code. In the next step, we will parallelize the algorithm with different perspectives of GASPI and MPI, so we obtain two codes with different parallelization strategy to compare their performance.
I’m sure that this summer we all will learn a lot. I will let you know about the updates.