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Title: GitHub - gpu-mode/awesomeMLSys: An ML Systems Onboarding list

Open Graph Title: GitHub - gpu-mode/awesomeMLSys: An ML Systems Onboarding list

X Title: GitHub - gpu-mode/awesomeMLSys: An ML Systems Onboarding list

Description: An ML Systems Onboarding list. Contribute to gpu-mode/awesomeMLSys development by creating an account on GitHub.

Open Graph Description: An ML Systems Onboarding list. Contribute to gpu-mode/awesomeMLSys development by creating an account on GitHub.

X Description: An ML Systems Onboarding list. Contribute to gpu-mode/awesomeMLSys development by creating an account on GitHub.

Opengraph URL: https://github.com/gpu-mode/awesomeMLSys

X: @github

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https://patch-diff.githubusercontent.com/gpu-mode/awesomeMLSys#attention-mechanism
Attention is all you needhttps://arxiv.org/abs/1706.03762
Online normalizer calculation for softmaxhttps://arxiv.org/abs/1805.02867
Self Attention does not need O(n^2) memoryhttps://arxiv.org/abs/2112.05682
Flash Attention 2https://arxiv.org/abs/2307.08691
Llama 2 paperhttps://arxiv.org/abs/2307.09288
gpt-fasthttps://github.com/pytorch-labs/gpt-fast
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationhttps://arxiv.org/abs/2108.12409
https://patch-diff.githubusercontent.com/gpu-mode/awesomeMLSys#performance-optimizations
Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systemshttps://arxiv.org/abs/2312.15234
Efficiently Scaling transformer inferencehttps://arxiv.org/abs/2211.05102
Making Deep Learning go Brrr from First Principleshttps://horace.io/brrr_intro.html
Fast Inference from Transformers via Speculative Decodinghttps://arxiv.org/abs/2211.17192
Group Query Attentionhttps://arxiv.org/pdf/2305.13245
Orca: A Distributed Serving System for Transformer-Based Generative Modelshttps://www.usenix.org/conference/osdi22/presentation/yu
Efficient Memory Management for Large Language Model Serving with PagedAttentionhttps://arxiv.org/abs/2309.06180
Colfax Research Bloghttps://research.colfax-intl.com/blog/
Sarathi LLMhttps://arxiv.org/abs/2308.16369
Epilogue Visitor Treehttps://dl.acm.org/doi/10.1145/3620666.3651369
https://patch-diff.githubusercontent.com/gpu-mode/awesomeMLSys#quantization
A White Paper on Neural Network Quantizationhttps://arxiv.org/abs/2106.08295
LLM.int8https://arxiv.org/abs/2208.07339
FP8 formats for deep learninghttps://arxiv.org/abs/2209.05433
Smoothquanthttps://arxiv.org/abs/2211.10438
Mixed precision traininghttps://arxiv.org/abs/1710.03740
https://patch-diff.githubusercontent.com/gpu-mode/awesomeMLSys#long-context-length
RoFormer: Enhanced Transformer with Rotary Position Embeddinghttps://arxiv.org/abs/2104.09864
YaRN: Efficient Context Window Extension of Large Language Modelshttps://arxiv.org/abs/2309.00071
Ring Attention with Blockwise Transformers for Near-Infinite Contexthttps://arxiv.org/abs/2310.01889
https://patch-diff.githubusercontent.com/gpu-mode/awesomeMLSys#sparsity
Venomhttps://arxiv.org/pdf/2310.02065
Megablockshttps://arxiv.org/pdf/2211.15841
ReLu Strikes Backhttps://openreview.net/pdf?id=osoWxY8q2E
https://patch-diff.githubusercontent.com/gpu-mode/awesomeMLSys#distributed
Singularityhttps://arxiv.org/abs/2202.07848
Local SGDhttps://arxiv.org/abs/1805.09767
OpenDilocohttps://arxiv.org/abs/2407.07852
torchtitanhttps://arxiv.org/abs/2410.06511
pipedreamhttps://arxiv.org/abs/1806.03377
jit checkpointinghttps://dl.acm.org/doi/pdf/10.1145/3627703.3650085
Reducing Activation Recomputation in Large Transformer modelshttps://arxiv.org/abs/2205.05198
Breaking the computation and communication abstraction barrierhttps://arxiv.org/abs/2105.05720
ZeRO: Memory Optimizations Toward Training Trillion Parameter Modelshttps://arxiv.org/abs/1910.02054
Megatron-LMhttps://arxiv.org/abs/1909.08053
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