Optimizing Compute-Intensive Serverless Workloads with Multi-threaded Rust on AWS Lambda

This article demonstrates how to optimize CPU-intensive AWS Lambda workloads using multi-threaded Rust, achieving significant performance improvements through parallel processing.

• Lambda allocates proportional CPU (up to 6 vCPUs at 10,240 MB), but sequential code only uses one vCPU
• Multi-threading with Rayon library enables parallel processing across available vCPUs
• Bcrypt password hashing used as CPU-intensive test workload for benchmarking
• ARM64 Graviton2 achieved 6.73x speedup with 6 workers; x86_64 achieved 5.72x speedup
• Cold start times consistently 19-28ms across all configurations
• ARM64 2048 MB configuration offers lowest cost at $36.46 per million invocations
• Thread pool initialized once at cold start to prevent race conditions
• Rayon's `.par_iter()` converts sequential iterators to parallel with minimal code changes
• Best for batch processing, cryptography, image/video processing, scientific computing
• Not recommended for I/O-bound operations or simple transformations under 100ms

Multi-threaded Rust on Lambda enables near-linear CPU scaling for compute-intensive workloads, delivering both performance gains and cost efficiency compared to sequential implementations.