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deepmind.google·6d ago
AI Engineering
Lobsters·Jun 15·5 min read
Clojure is almost as fast as C (with some help)
TL;DR
Clojure's performance lagged behind C due to the lack of automatic vectorization in the Java Virtual Machine (JVM). By utilizing the Vector API from Project Panama, SIMD operations were manually implemented in Clojure.
✦ Why It Matters
Engineers can leverage the Vector API in Clojure to enhance performance in compute-intensive applications.
Key Takeaways
- Explore the Vector API for performance improvements in Clojure.
- Consider manual SIMD implementation for critical loops.
- Benchmark performance against optimized C code.
- Utilize type hints and unchecked math for optimization.
Full Summary
Clojure, a functional programming language that runs on the Java Virtual Machine (JVM), was tested against a C implementation for a stress test involving 100,000 cubes in space. The C version, optimized with clang at-O2, leveraged automatic vectorization to efficiently compute transformation matrices.
Initial benchmarks showed Clojure's best attempt took 2.6 ms per frame, nearly four times slower than C's 0.70 ms. To bridge this performance gap, the Vector API from Project Panama was employed, allowing developers to manually implement SIMD (Single Instruction, Multiple Data) operations in Clojure.
This method provided a way to optimize performance without relying solely on the JVM's Just-In-Time (JIT) compiler. The results indicated that with the Vector API, Clojure could achieve performance closer to that of C, making it a viable option for high-performance applications.
This finding highlights the potential for functional languages to compete in performance-critical domains.