Algorithms + Data Structures = Programs Episode 242: Thrust & Parallel Algorithms (Part 4)
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Jul 11, 2025 Jared Hoberock, co-creator of the Thrust parallel algorithms library at NVIDIA, shares insights into the evolution and challenges of Thrust's development. He discusses debugging practices and race conditions, illustrating the complexity of the programming landscape. The conversation delves into future innovations in programming languages, highlighting efforts to streamline software development. With a mix of technical depth and light-hearted anecdotes, Hoberock offers a fascinating look at computer graphics and the programming world.
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Thrust's Key to Success
- Thrust succeeded mainly because it chose the right interface for parallel algorithms.
- Early talks like those at GTC attracted large, enthusiastic crowds, indicating rapid adoption.
Surprise at Thrust's Popularity
- Jared didn't realize how widely used Thrust was until hearing from others outside the original team.
- Productizing Thrust made it less flexible and harder to change rapidly than during initial development.
Thrust's Asynchrony Limitations
- Thrust lacks an asynchronous execution model, which limits its use in complex multi-kernel applications.
- The absence of effective CUDA streams support is a key limitation and missed opportunity for Thrust.