ComputeFit for Researchers
A web app where university researchers and indie scientists describe their workload ('train a small transformer on 50k samples', 'solve a 10k×10k linear system') and instantly see whether their laptop is enough, what runtime to expect, and which free numerical libraries (OpenBLAS, AOCL, MKL) will give them the biggest speedup on their specific CPU.
Graduate students and academic researchers running numerical experiments on personal laptops without HPC cluster access
- Workload-to-hardware estimator that maps problem size to expected runtime on common laptop chips
- Library recommendation engine that picks OpenBLAS vs AOCL vs a hand-tuned kernel like the repo's MX24
- 'Email me when this finishes' job runner for longer benchmarks
- Exportable methods section text justifying the chosen configuration
The HN-trending repo proved a tuned single-core GEMM can match OpenBLAS; researchers without HPC access want a way to know if they can get away with their laptop before paying for cluster time.
Exact pain point validated by a Medium article literally titled 'Can this be done on my laptop, or do I require HPC resources?' plus recurring 'best laptops for researchers' buyer-guide content, but it's a periodic rather than daily question.Can this be done on my laptop, or do I require HPC resources? ↗6 Best Laptops for Researchers in 2025 | Tech Consumer Guide ↗
No direct consumer-facing 'will my laptop run X' tool found; existing runtime-prediction research (arxiv 2504.20867) targets HPC cluster scheduling, and Scopeslab targets software-effort estimation — the laptop-feasibility niche appears wide open.Predicting the Performance of Scientific Workflow Tasks for Cluster ... ↗Scopeslab - AI-Powered Workload Estimation Platform ↗
Graduate students and academics are notoriously price-sensitive; OpenBLAS/MKL/AOCL themselves are free and the value delivered is a one-time lookup, making recurring SaaS willingness-to-pay weak.Accelerated Linear Algebra Libraries (MKL and OpenBLAS) ↗
Numerical/scientific computing on personal laptops is a durable need as datasets grow, but specific BLAS-library recommendations churn with each CPU generation, capping the half-life of the advice.AMD 9950x dgemm benchmarks OPENBLAS/BLIS/MKL ↗
Buildable with browser-side CPU feature detection, a curated benchmark table, and simple workload models — no ML training or external API required, though accurate runtime estimates need empirical calibration.Performance Comparison of OpenBLAS* and Intel® oneAPI Math Kernel Library ↗