CascMult: A Beginner’s Guide to Getting Started

Optimizing Performance in CascMult: Tips and Benchmarks

Overview

CascMult performance depends on algorithmic configuration, data layout, concurrency, and I/O. Below are targeted optimizations and a simple benchmarking approach you can run to measure impact.

Key optimization tips

• Choose the right algorithmic mode: Use the lower-complexity mode for mostly dense inputs and the cache-friendly mode for large sparse inputs.
• Data layout: Store inputs in contiguous buffers (row-major or column-major consistently) to maximize vectorized loads and reduce cache misses.
• Batching: Process data in larger batches to amortize overheads, but cap batch size before cache thrashing; pick the largest size that fits L3 cache.
• Threading and concurrency: Use a thread pool sized to CPU cores × (0.8–1.5) depending on I/O wait; prefer work-stealing schedulers to balance load across uneven tasks.
• Vectorization: Compile with high optimization (e.g., -O3) and enable architecture-specific vector extensions (AVX2/AVX-512) if available; align buffers to ⁄₆₄ bytes.
• Memory allocation: Reuse pooled buffers; avoid frequent allocations/deallocations during hot loops. Use page-aligned allocators for large buffers.
• I/O and serialization: Stream inputs rather than loading everything into memory if working sets exceed RAM; use lightweight binary formats and compress only when CPU is underutilized.
• Parameter tuning: Tune internal thresholds (tiling sizes, recursion depth, cutoff for switching algorithms) using automated search (grid or Bayesian optimization).
• Profiling-driven changes: Profile both CPU (hot loops) and memory (cache-misses, TLB) before changing code; focus on top 20% of hotspots that cost ~80% runtime.

Simple benchmark plan

1. Define representative workloads (small, medium, large; dense vs sparse).
2. Baseline: run with default settings; capture runtime, throughput, CPU utilization, memory, cache-misses.
3. Isolate changes: apply one optimization at a time (e.g., enable vectorization) and rerun workloads.
4. Measure scaling: vary thread counts and batch sizes to find sweet spot.
5. Report: present runtime, throughput (items/sec), speedup vs baseline, and resource usage.

Example benchmark table (format to collect)

• Workload | Threads | Batch size | Runtime (s) | Items/sec | Speedup | Peak memory | L3 cache-miss %
• small | 4 | 1k | … | … | … | … | …
• medium | 8 | 16k | … | … | … | … | …
• large | 16 | 64k | … | … | … | … | …

Common bottlenecks & fixes

• High cache-miss rate: reduce working set per thread, improve tiling, align data.
• Imbalanced threads: use dynamic scheduling or smaller tasks.
• Excessive allocations: switch to buffer pools.
• I/O-bound runs: overlap compute with asynchronous I/O or increase prefetching.

Quick checklist to run now

• Compile with optimizations and CPU-specific flags.
• Align and pack input buffers.
• Start with thread_count = CPU cores, then sweep ±50%.
• Benchmark small→large workloads and collect cache metrics.
• Apply one change at a time and keep the best configuration.

If you want, I can (choose one): produce a bash benchmarking script, suggest compiler flags for a specific CPU, or draft a tuning matrix for automated parameter search.