JuliaParallel/Dagger.jl: A framework for out-of-core and parallel execution

A framework for out-of-core and parallel execution

A framework for out-of-core and parallel computing Documentation Build Status What is Dagger? Dagger.jl automatically speeds up your Julia code by running tasks in parallel across all your CPU cores, GPUs, and even multiple computers, without you having to manage the complex coordination yourself. Just wrap your function calls with Dagger.@spawn (similar to @async ), and Dagger figures out the optimal way to distribute the work and move data around, making it especially powerful for nested loops, large datasets, and GPU acceleration that would otherwise require extensive manual parallelization

related reading

• Together AI | The AI Native Cloudtogether.ai
• DP, DPA and SGLang DP Router - SGLang Documentationdocs.sglang.io
• CodaLab Worksheetsworksheets.codalab.org
• How to think in JAX — JAX documentationdocs.jax.dev
• irhum.github.io - Tensor Parallelism with jax.pjitirhum.github.io
• Embarrassingly parallel - Wikipediaen.wikipedia.org
• Autoresearchgivemeanode.com
• Parallelism in Distributed Deep Learning · Better Tomorrow with Computer Scienceinsujang.github.io
• Pipeline-Parallelism: Distributed Training via Model Partitioningsiboehm.com
• How to Parallelize a Transformer for Training — an explorable explanationezyang.github.io
• GitHub - jax-ml/jax: Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and moregithub.com
• Why You Should (or Shouldn't) be Using Google's JAX in 2023assemblyai.com