--- title: "Compress release artifacts in parallel" description: "Compress independent release binaries two at a time." url: https://starsling.dev/ci-playbooks/techniques/parallel-packaging canonicalUrl: https://starsling.dev/ci-playbooks/techniques/parallel-packaging --- # Compress release artifacts in parallel Compress independent release binaries two at a time. ## How to choose an approach This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it. - **[OpenAI](https://starsling.dev/ci-playbooks/openai#parallel-packaging)**: Use it when A release packaging step compresses or archives independent files one after another. Check before adopting: More workers compete for CPU and disk on the same runner; measure the worker count, and keep the step failing when any worker fails. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Compress release artifacts in parallel. Compress independent release binaries two at a time. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/parallel-packaging.md Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence. Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured. Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo. Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES. Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch. Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge. ```` ## How OpenAI used this technique Read what OpenAI changed and follow the original sources. ### OpenAI: Compress release artifacts in parallel Codex replaced the loop that archived each Windows release binary in turn with xargs running two workers, so independent binaries compress at the same time. Evidence: Source PR/commit evidence Compression step: serial 121 s, 123 s, 121 s against parallel 73 s, 73 s, 74 s, three trials each on a Windows x64 release runner ([Source](https://github.com/openai/codex/pull/27855)) Use it when: A release packaging step compresses or archives independent files one after another. Before applying it: More workers compete for CPU and disk on the same runner; measure the worker count, and keep the step failing when any worker fails. [Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#parallel-packaging) [Primary source](https://github.com/openai/codex/pull/27855) ## CI optimization is continuous work. Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on. [Get self-improving CI](https://starsling.dev/products/runners) ## Related techniques ### [Clean up independent test resources concurrently](https://starsling.dev/ci-playbooks/techniques/parallel-cleanup) Independent index deletions do not need a separate serial wait per index. 1 implementation · 1 company (Mastra) ### [Let shared packages without a build step stay cacheable](https://starsling.dev/ci-playbooks/techniques/uncluster-no-op-projects) Treat a package with nothing to build as cacheable, so its dependents spread across every CI agent. 1 implementation · 1 company (DoorDash) ### [Overlap independent setup with explicit joins](https://starsling.dev/ci-playbooks/techniques/overlap-independent-setup) Pull images while dependencies install, and wait for each background step explicitly. 1 implementation · 1 company (Stripe) ## Company Playbooks ### [Anthropic](https://starsling.dev/ci-playbooks/anthropic) Run only affected tests ### [DoorDash](https://starsling.dev/ci-playbooks/doordash) Run build and test in parallel, sharded across CI agents ### [Linear](https://starsling.dev/ci-playbooks/linear) Benchmark the same job on faster hardware ### [Mastra](https://starsling.dev/ci-playbooks/mastra) StarSling runners for the slowest test workflow ### [OpenAI](https://starsling.dev/ci-playbooks/openai) Rank CI fixes by the minutes they save ### [Ramp](https://starsling.dev/ci-playbooks/ramp) Move a CPU-bound test suite to a faster CPU ### [Shopify](https://starsling.dev/ci-playbooks/shopify) Let an agent iterate against one CI metric ### [Spotify](https://starsling.dev/ci-playbooks/spotify) Build arm64 on native arm64 runners ### [Stripe](https://starsling.dev/ci-playbooks/stripe) Run only the tests a change can reach ### [Uber](https://starsling.dev/ci-playbooks/uber) Build the merge-queue speculations most likely to be needed ## Related guides Implementation guides for this technique. ### [Shard tests across parallel jobs](https://starsling.dev/best-practices/github-actions/shard-tests) Use native test sharding and a job matrix to divide a long suite. ### [Parallelize independent jobs](https://starsling.dev/github-actions/optimizations/parallelize-independent-jobs) Run independent validation lanes concurrently and keep genuine job dependencies.