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Developing custom programming languages for optimized visual AI systems | MIT News

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In Short:

MIT Professor Jonathan Ragan-Kelley is revolutionizing programming languages to match the increasingly complex hardware in computers today. By designing tools like the Halide programming language, he enables efficient photo editing and processing. His research focuses on optimizing programs for specialized hardware like GPUs and accelerators, bridging the gap between general-purpose computing and tailored programs. Ragan-Kelley’s innovative work aims to unlock the full potential of modern machines.


A single photograph provides insights into the creator’s world – their interests and feelings towards a subject or space. But what about the creators behind the technologies that enable these images to come to life?

MIT Department of Electrical Engineering and Computer Science

Associate Professor Jonathan Ragan-Kelley is one such individual who has crafted tools for visual effects in movies and developed the Halide programming language, widely used in the industry for photo editing and processing. With a focus on high-performance, domain-specific programming languages and machine learning, Ragan-Kelley’s research enables advancements in 2D and 3D graphics, visual effects, and computational photography.

Driving Innovation

Ragan-Kelley emphasizes the need for new programming languages that can efficiently utilize the complex hardware present in modern computers. As technology shifts towards specialized computing units like GPUs and accelerators, there is a growing demand for tailored programs and compilers to maximize performance.

His work involves striking a balance between capturing the structure of specific computational problems for enhanced efficiency while also automating the mapping of programs to hardware. Projects like Halide and Exo showcase his expertise in optimizing performance for various applications, from image processing to machine learning.

Innovative Solutions for Efficiency

Ragan-Kelley’s team explores cutting-edge techniques, including machine learning for optimized schedules and “exocompilation” to customize compiler controls. By rethinking large language models and fine-tuning computation architectures, they aim to enhance efficiency on AI hardware without compromising accuracy.

His forward-thinking approach towards computation efficiency and hardware optimization sets him apart in the field. By pursuing unconventional ideas with significant practical impact, Ragan-Kelley aims to unlock the full computational potential of new machines and drive innovation in software performance engineering.

His teaching of course 6.106 on Software Performance Engineering reflects MIT’s commitment to preparing students for the complexities of modern hardware. By adapting programming practices, Ragan-Kelley believes we can propel the development of new applications that harness the power of advanced hardware.

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