# Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures

arXiv: https://arxiv.org/abs/2304.12576 · HTML: https://mlsanity.com/paper/2304.12576/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 58 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [Ansor: Generating High-Performance Tensor Programs for Deep Learning](https://mlsanity.com/paper/2006.06762/) (2020)
- [Block Pruning For Faster Transformers](https://mlsanity.com/paper/2109.04838/) (2021)
- [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://mlsanity.com/paper/2203.07259/) (2022)
- [Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions](https://mlsanity.com/paper/1802.04730/) (2018)
- [Deep Learning for Computational Chemistry](https://mlsanity.com/paper/1701.04503/) (2017)
- [A Survey of Deep Learning for Scientific Discovery](https://mlsanity.com/paper/2003.11755/) (2020)
- [Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks](https://mlsanity.com/paper/1301.3605/) (2013)
- [Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures](https://mlsanity.com/paper/1808.05567/) (2018)
- [The ARM Scalable Vector Extension](https://mlsanity.com/paper/1803.06185/) (2018)
- [Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads](https://mlsanity.com/paper/2104.05755/) (2021)
- [oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation](https://mlsanity.com/paper/2301.01333/) (2023)
- [Stripe: Tensor Compilation via the Nested Polyhedral Model](https://mlsanity.com/paper/1903.06498/) (2019)
- [A Survey on Domain-Specific Languages for Machine Learning in Big Data](https://mlsanity.com/paper/1602.07637/) (2016)

## Built on by (most cited first, 5 of 5)

- [ML-SpecQD: Multi-Level Speculative Decoding with Quantized Drafts](https://mlsanity.com/paper/2503.13565/) (2025)
- [Towards a high-performance AI compiler with upstream MLIR](https://mlsanity.com/paper/2404.15204/) (2024)
- [DECA: A Near-Core LLM Decompression Accelerator Grounded on a 3D Roofline Model](https://mlsanity.com/paper/2505.19349/) (2025)
- [Pushing the Envelope of LLM Inference with Ultra-Low-Bit Quantized Models](https://mlsanity.com/paper/2508.06753/) (2025)
- [Space Filling Curves is All You Need: Communication-Avoiding Matrix Multiplication Made Simple](https://mlsanity.com/paper/2601.16294/) (2026)

## Abstract (arXiv)

During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance Computing (HPC) counterparts. Nevertheless, the programming methodology of DL and HPC systems is stagnant, relying on highly-optimized, yet platform-specific and inflexible vendor-optimized libraries. Such libraries provide close-to-peak performance on specific platforms, kernels and shapes thereof that vendors have dedicated optimizations efforts, while they underperform in the remaining use-cases, yielding non-portable codes with performance glass-jaws. This work introduces a framework to develop efficient, portable DL and HPC kernels for modern CPU architectures. We decompose the kernel development in two steps: 1) Expressing the computational core using Tensor Processing Primitives (TPPs): a compact, versatile set of 2D-tensor operators, 2) Expressing the logical loops around TPPs in a high-level, declarative fashion whereas the exact instantiation (ordering, tiling, parallelization) is determined via simple knobs. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.