ppopp-experiments
Use when designing or auditing a PPoPP paper's evaluation, covering the twin bar of concurrency correctness and measured scalability — speedup curves, strong vs weak scaling, core/thread sweeps, NUMA and GPU effects, contention microbenchmarks plus real workloads, variance and measurement hygiene, and honest strong baselines.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ppopp-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PPoPP Experiments Design the evaluation to clear PPoPP's **twin bar**: the contribution must be **correct under concurrency** and **measurably scalable**. A speedup with no correctness argument, or a correctness proof with no scaling data, each fails half the venue. Reviewers are parallel-systems experts who will interrogate the baseline, the machine, and the variance before they believe a number. ## Match evidence to the claim | Claim shape | Evidence PPoPP expects | Common failure it catches | |---|---|---| | A lock-free/wait-free structure | Throughput vs. thread count under varied contention; a linearizability/progress argument; memory-reclamation overhead | Single contention level; "no race seen" instead of an argument | | A parallel runtime/scheduler | Overhead vs. sequential; strong+weak scaling on real workloads; load-balance behavior | Microbenchmarks only; no real application | | A GPU/accelerator technique | Speedup over a strong GPU baseline; occupancy/divergence analysis; transfer costs counted | Ignoring host-device transfer; a weak baseline kernel | | A parallel algorithm | Scaling on real inputs; NUMA/locality effects; comparison to the best known implementation |
- Match evidence to the claim
- The scalability story
- Correctness under concurrency
- Baselines that survive scrutiny
- Measurement hygiene
- Anticipate the rebuttal questions at design time
- Output format
What does the ppopp-experiments skill do?
Use when designing or auditing a PPoPP paper's evaluation, covering the twin bar of concurrency correctness and measured scalability — speedup curves, strong vs weak scaling, core/thread sweeps, NUMA and GPU effects, contention microbenchmarks plus real workloads, variance and measurement hygiene, and honest strong baselines.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ppopp-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 984 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.