Artur Bialas - Graphics Software Engineer at Intel Corporation

Artur Bialas

Graphics Software Engineer at Intel Corporation

Gdańsk, Pomeranian Voivodeship, Poland

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Summary

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Rockstar

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Top School

Artur Bialas is a Graphics Software Engineer with nine years of experience building high-performance GPU software and deep learning inference libraries at Intel. He specializes in GPGPU, contributing both optimized primitives and graph-level optimizations for clDNN and implementing MLIR/Metal-related compiler passes in open-source projects like PlaidML. His background spans low-level kernel tuning, bug triage and validation for OpenCL, and cross-geo collaboration on driver test automation—skills that bridge research-grade algorithms and production-ready implementations. Based in Gdańsk, he pairs an M.Sc. in Electronics and Communications Engineering with practical experience exposing new operations and improving compilation and storage strategies in widely used ML compute libraries.

9 years of coding experience

7 years of employment as a software developer

Master of Science (M.Sc.), Electronics and Communications Engineering, Master of Science (M.Sc.), Electronics and Communications Engineering at Gdansk University of Technology

Polish, English, German

Github Skills (12)

opencl10

code-optimization10

c-language10

deep-learning10

cprogramming-language10

deep-neural-networks10

intel9

kernel-mode9

kernel9

convolution9

spirv9

deep-q-learning7

Programming languages (2)

C++LLVM

Github contributions (5)

plaidml/plaidml

Aug 2020 - Jan 2021

PlaidML is a framework for making deep learning work everywhere.

Role in this project:

Back-end Developer

Contributions:34 reviews, 23 commits, 30 PRs in 5 months

Contributions summary:Artur primarily contributed to the `plaidml/plaidml` repository by implementing and modifying passes for the Metal Language IR (MLIR) dialect. Their work focused on transforming and optimizing code, specifically related to i1 storage and subgroup operations. This included adding and modifying passes to convert i1 storage to i32 and to enable subgroup broadcast operations within the framework. The user's changes involved code modifications to C++ files and MLIR dialect files to facilitate these transformations.

deep-learningplaidml

intel/clDNN

May 2017 - Sep 2018

Compute Library for Deep Neural Networks (clDNN)

Role in this project:

Back-end Developer

Contributions:6 commits in 1 year 3 months

Contributions summary:Artur's contributions primarily focused on fixing compilation issues and improving the overall functionality of the Compute Library for Deep Neural Networks (clDNN). They addressed layout issues, corrected engine info parameters, and exposed new operations in the eltwise API. Additionally, they modified kernels for improved performance and changed learning parameter order within the convolution depthwise separable optimized kernel.

deep-neural-networksdeep-learningintelintel-hd-graphicscldnn

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