parallelization – Andrew Fairless, Ph.D.

What I Read: SSD, Database

By Andrew Fairless on September 12, 2024June 29, 2024

https://cedardb.com/blog/ssd_latency Why Your SSD (Probably) Sucks and What Your Database Can Do About ItLukas VogelJune 18, 2024 “SSDs have effectively replaced spinning disks as the go-to solution for persistent storageContinue readingWhat I Read: SSD, Database

What I Read: Command-line Tools, Faster

By Andrew Fairless on September 10, 2024June 29, 2024

https://adamdrake.com/command-line-tools-can-be-235x-faster-than-your-hadoop-cluster.html Command-line Tools can be 235x Faster than your Hadoop ClusterAdam Drake “One especially under-used approach for data processing is using standard shell tools and commands. The benefits of thisContinue readingWhat I Read: Command-line Tools, Faster

What I Read: Scaling to Multi-Terabyte Datasets

By Andrew Fairless on September 9, 2024June 29, 2024

Lessons Learned from Scaling to Multi-Terabyte Datasets v2thegreat, AliLessons Learned from Scaling to Multi-Terabyte DatasetsJune 19, 2024 “This post is meant to guide you through some of the lessons I’ve learnedContinue readingWhat I Read: Scaling to Multi-Terabyte Datasets

What I Read: How fast process CSV file

By Andrew Fairless on May 8, 2024March 7, 2024

https://datapythonista.me/blog/how-fast-can-we-process-a-csv-file How fast can we process a CSV fileMarc GarciaThu 22 February 2024 “…we’ll see in this blog post how to process a CSV file as fast as possible.”

What I Read: Mamba

By Andrew Fairless on May 1, 2024March 7, 2024

https://jameschen.io/jekyll/update/2024/02/12/mamba.html Mamba No. 5 (A Little Bit Of…)James ChenFeb 12, 2024 “…I attempt to provide a walkthrough of the essence of the Mamba state space model architecture, occasionally sacrificing someContinue readingWhat I Read: Mamba

What I Read: GPU Computing

By Andrew Fairless on January 15, 2024November 7, 2023

https://codeconfessions.substack.com/p/gpu-computing What Every Developer Should Know About GPU ComputingAbhinav UpadhyayOct 18, 2023 “GPUs have become incredibly important because of their pervasive use in deep learning. Today, it is essential forContinue readingWhat I Read: GPU Computing

What I Read: Retrieval Augmented Generation at scale

By Andrew Fairless on December 13, 2023November 7, 2023

https://medium.com/@neum_ai/retrieval-augmented-generation-at-scale-building-a-distributed-system-for-synchronizing-and-eaa29162521 Retrieval Augmented Generation at scale — Building a distributed system for synchronizing and ingesting billions of text embeddingsNeum AISep 28 “…getting a Retrieval Augmented Generation (RAG) application started isContinue readingWhat I Read: Retrieval Augmented Generation at scale

What I Read: Accelerating PyTorch

By Andrew Fairless on September 6, 2023August 1, 2023

https://magazine.sebastianraschka.com/p/accelerating-pytorch-model-training Accelerating PyTorch Model TrainingUsing Mixed-Precision and Fully Sharded Data ParallelismSebastian Raschka, PhDJun 26, 2023 “…how to scale PyTorch model training with minimal code changes. The focus here is onContinue readingWhat I Read: Accelerating PyTorch

What I Read: How to Train Really Large Models

By Andrew Fairless on October 26, 2021October 16, 2021

https://lilianweng.github.io/lil-log/2021/09/24/train-large-neural-networks.html How to Train Really Large Models on Many GPUs?Sep 24, 2021Lilian Weng “How to train large and deep neural networks is challenging, as it demands a large amount ofContinue readingWhat I Read: How to Train Really Large Models

What I Read: Systems for Machine Learning

By Andrew Fairless on September 14, 2021August 20, 2021

https://thegradient.pub/systems-for-machine-learning/ Systems for Machine LearningKabir Nagrecha14.Aug.2021 “Machine learning’s increasing importance to real-world applications brought awareness of a new field focused on ML in practice – machine learning systems (or, asContinue readingWhat I Read: Systems for Machine Learning

Tag: parallelization