About Me
I am a postdoctoral researcher at the University of Münster in Germany. My work focuses on compiler technologies—code generation and optimization—as well as programming language design for parallel architectures, including GPUs and CPUs, based on formal methods. My central research goal is to advance Performance, Portability, and Productivity for data-parallel computations, with a particular emphasis on computations relevant to deep learning (e.g., linear algebra routines and stencil computations).
To pursue my research goal, I am the principal designer of a holistic code Generation & Optimization & Execution approach, structured into three major sub-projects:
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Multi-Dimensional Homomorphisms (MDH) (Code Generation)—a novel algebraic formalism for expressing and reasoning formally about optimizations for data-parallel computations. This project includes the design and specification of a Domain-Specific Language (DSL) for expressing data-parallel computations, as well as the development of a compiler for this DSL. Our compiler generates code (e.g., in CUDA, OpenMP, and OpenCL) that can be automatically optimized (auto-tuned) for contemporary parallel architectures.
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Auto-Tuning Framework (ATF) (Code Optimization)—a general-purpose auto-tuning approach that automatically optimizes parallel programs with constrained optimization parameters. For this, ATF introduces novel processes for generating, storing, and exploring the search spaces of constrained optimization parameters.
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Host Code Abstraction (HCA) (Code Execution)—a high-level programming abstraction that simplifies implementing and optimizing so-called host code which is required in modern parallel programming approaches (e.g., CUDA and OpenCL) to execute code on the devices of (potentially distributed) multi-device systems.
You can find my CV here.