Halide: A Language for Fast, Portable Computation on Images and Tensors

https://cppcon.org/ https://github.com/CppCon/CppCon2020/blob/main/Presentations/halide_a_language_for_fast_portable_computation_on_images_and_tensors/halide_a_language_for_fast_portable_computation_on_images_and_tensors__alex_reinking__cppcon_2020.pptx

Halide is an open-source, domain-specific language for optimizing image processing, machine learning, and general array processing. It is used by major companies like Google, Adobe, and Qualcomm to optimize performance-critical software. It processes every photo taken with a Pixel phone, composites layers in every Photoshop document, and handles video processing at scale at YouTube.

We will explore how Halide achieves top-tier performance in a fraction of the development time by separating the algorithm (what to compute) from the schedule (how to optimize it). Halide’s schedules determine trade-offs between parallelism, vectorization, cache locality, and memory management in a simple, modular way. Programmers can easily target different CPUs and GPUs by writing multiple schedules. Halide integrates tightly with C++ and provides both a JIT and a C++ compatible ahead-of-time compiler.

Alex is a Ph.D. student at UC Berkeley working with Jonathan Ragan-Kelley on designing domain-specific languages for high-performance computing. He has previously worked at Microsoft Research and Facebook AI+R. He holds an MS in Computer Science from UC Berkeley and a BS in Computer Science and Mathematics from Yale.

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