# keroro824/HashingDeepLearning

Codebase for "SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems"

Repository: https://github.com/keroro824/HashingDeepLearning
Canonical: https://ross.abutalabs.com/products/hashingdeeplearning
Language: C++
License: MIT
License Family: permissive
Last push: 2021-04-13T01:32:24+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2765, "days_push": 1969, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1103, forks 168 (observed 2026-08-28T04:03:36.137034+00:00)

## What it is
SLIDE is a C++ research codebase implementing locality-sensitive hashing based training of deep neural networks, from the paper 'In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems'. It demonstrates that smart algorithmic sampling can outperform GPU baselines on CPUs for large-scale MLP training, with TensorFlow baselines and the Amazon-670K dataset included.

## Use cases
- reproduce SLIDE paper experiments on large-scale deep learning
- train MLPs on extreme multi-label classification datasets like Amazon-670K
- compare LSH-based sampling against full and sampled softmax in TensorFlow
- run deep learning training faster on CPUs instead of GPUs
- study hashing-based sparse network training algorithms

## When to choose
- you want to reproduce or extend the SLIDE research results
- you need CPU-only training of large sparse MLPs with LSH sampling
- you are researching algorithmic alternatives to hardware acceleration

## When to avoid
- you need a maintained production deep learning framework - use the newer RUSH-LAB/SLIDE or PyTorch
- you train CNNs or transformers rather than MLP-style networks
- your hardware lacks Skylake+ huge pages support and you cannot modify build flags

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning, llm-training, gpu-computing
- domain: deep-learning, machine-learning, large-language-models
- platform: cpp, python
- tags: lsh, locality-sensitive-hashing, sparse-mlp, cpu-optimization, research-code, extreme-classification, algorithms, linux, docker

## Member repositories
- keroro824/HashingDeepLearning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:36.137034+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:45:33.423311+00:00, confidence not recorded.
  - readme: https://github.com/keroro824/HashingDeepLearning (fetched 2026-08-28T04:03:36.137034+00:00, sha e0b9825851b2)
- Data as of 2026-08-30T08:39:29.467469+00:00.