Can this skill help with memory management in Rust?

Absolutely. It focuses on reducing allocations through pre-allocation, reusing objects, and avoiding unnecessary clones via zero-copy techniques.

Does this skill provide specific tool recommendations for Rust?

Yes, it recommends industry-standard tools like Criterion for benchmarking, Flamegraph for CPU profiling, and Heaptrack for tracking memory allocations.

Does it support parallelization strategies?

Yes, it provides guidance on using Rayon for data parallelism and selecting between threads or async for I/O-bound vs CPU-bound tasks.

How does the skill handle the trade-off between speed and complexity?

The skill includes a 'Thinking Prompt' that asks developers to evaluate if an optimization is worth the added complexity based on measured hotspots and business SLAs.

Rust Performance Optimization

Name: Rust Performance Optimization
Author: lywa1998

bylywa1998

Herramientas para Desarrolladores

Optimizes Rust application performance through systematic measurement, profiling, and high-efficiency implementation patterns.

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The m10-performance skill provides a rigorous framework for identifying and resolving bottlenecks in Rust applications. It guides developers through a tiered optimization strategy, starting with empirical measurement using tools like Criterion and Flamegraph before moving into high-impact design choices. By focusing on memory pre-allocation, cache-friendly data structures, zero-copy operations, and SIMD/parallelism, this skill ensures that performance improvements are data-driven and effectively balance execution speed with code maintainability.

Características Principales

Systematic profiling guidance using Flamegraph, perf, and heaptrack.
0 GitHub stars
Memory allocation reduction strategies including SmallVec and Cow patterns.
Cache efficiency optimization for contiguous data and sequential access.
Statistical benchmarking workflows with Criterion and cargo bench.
Identification and correction of performance anti-patterns like hidden clones.

Casos de Uso

Optimizing memory usage and allocation frequency in embedded or resource-constrained systems.
Reducing tail latency in high-throughput Rust backend services.
Accelerating data-heavy computations using data parallelism and SIMD.

Acerca de

Características Principales

Systematic profiling guidance using Flamegraph, perf, and heaptrack.
0 GitHub stars
Memory allocation reduction strategies including SmallVec and Cow patterns.
Cache efficiency optimization for contiguous data and sequential access.
Statistical benchmarking workflows with Criterion and cargo bench.
Identification and correction of performance anti-patterns like hidden clones.

Casos de Uso

Optimizing memory usage and allocation frequency in embedded or resource-constrained systems.
Reducing tail latency in high-throughput Rust backend services.
Accelerating data-heavy computations using data parallelism and SIMD.