r-barnes,Richard Barnes,github

dump_gdbtable

Python script to dump the content of a .gdbtable

Dynolog is a telemetry daemon for performance monitoring and tracing. It exports metrics from different components in the system like the linux kernel, CPU, disks, Intel PT, GPUs etc. Dynolog also integrates with pytorch and can trigger traces for distributed training applications.

earthengine-api

Python and JavaScript bindings for calling the Earth Engine API.

ecoopt

ecos.jl

Julia wrapper for the ECOS conic optimization solver

emflow

Computing the drainage network on huge grid terrains

empirical_roofline_model_for_gpus

emptyepsilon

Open source bridge simulator. Build with the SeriousProton engine.

fah-control

Folding@home Client Advanced Control GUI

faster-unmixer

A faster implementation of the sediment inverse/unmixing scheme proposed in Lipp et al (2021).

fbgemm

FB (Facebook) + GEMM (General Matrix-Matrix Multiplication) - https://code.fb.com/ml-applications/fbgemm/

fbpcf

Private computation framework library allows developers to perform randomized controlled trials, without leaking information about who participated or what action an individual took. It uses secure multiparty computation to guarantee this privacy. It is suitable for conducting A/B testing, or measuring advertising lift and learning the aggregate st

fbpcs

FBPCS (Facebook Private Computation Solutions) leverages secure multi-party computation (MPC) to output aggregated data without making unencrypted, readable data available to the other party or any third parties. Facebook provides impression & opportunity data, and the advertiser provides conversion / outcome data.

fbtt-embedding

This is a Tensor Train based compression library to compress sparse embedding tables used in large-scale machine learning models such as recommendation and natural language processing. We showed this library can reduce the total model size by up to 100x in Facebook’s open sourced DLRM model while achieving same model quality. Our implementation is faster than the state-of-the-art implementations. Existing the state-of-the-art library also decompresses the whole embedding tables on the fly therefore they do not provide memory reduction during runtime of the training. Our library decompresses only the requested rows therefore can provide 10,000 times memory footprint reduction per embedding table. The library also includes a software cache to store a portion of the entries in the table in decompressed format for faster lookup and process.