# DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures

arXiv: https://arxiv.org/abs/2511.15503 · HTML: https://mlsanity.com/paper/2511.15503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 52 of its 151 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 30 of 52)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving](https://mlsanity.com/paper/2401.09670/) (2024)
- [Ansor: Generating High-Performance Tensor Programs for Deep Learning](https://mlsanity.com/paper/2006.06762/) (2020)
- [Learning to Optimize Tensor Programs](https://mlsanity.com/paper/1805.08166/) (2018)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [NeuPIMs: NPU-PIM Heterogeneous Acceleration for Batched LLM Inferencing](https://mlsanity.com/paper/2403.00579/) (2024)
- [SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning](https://mlsanity.com/paper/2207.04606/) (2022)
- [POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference](https://mlsanity.com/paper/2410.18038/) (2024)
- [TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning](https://mlsanity.com/paper/1908.03072/) (2019)
- [TensorIR: An Abstraction for Automatic Tensorized Program Optimization](https://mlsanity.com/paper/2207.04296/) (2022)
- [NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference](https://mlsanity.com/paper/2411.01142/) (2024)
- [A Modern Primer on Processing in Memory](https://mlsanity.com/paper/2012.03112/) (2020)
- [Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey](https://mlsanity.com/paper/2001.02319/) (2020)
- [Tensor Program Optimization with Probabilistic Programs](https://mlsanity.com/paper/2205.13603/) (2022)
- [Ramulator 2.0: A Modern, Modular, and Extensible DRAM Simulator](https://mlsanity.com/paper/2308.11030/) (2023)
- [Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation](https://mlsanity.com/paper/2001.08743/) (2020)
- [Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs](https://mlsanity.com/paper/2210.09603/) (2022)
- [Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks](https://mlsanity.com/paper/2109.14320/) (2021)
- [PIM Is All You Need: A CXL-Enabled GPU-Free System for Large Language Model Inference](https://mlsanity.com/paper/2502.07578/) (2025)
- [Processing Data Where It Makes Sense: Enabling In-Memory Computation](https://mlsanity.com/paper/1903.03988/) (2019)
- [A Tensor Compiler for Unified Machine Learning Prediction Serving](https://mlsanity.com/paper/2010.04804/) (2020)
- [PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System](https://mlsanity.com/paper/2502.15470/) (2025)
- [Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance](https://mlsanity.com/paper/2110.15238/) (2021)
- [SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems](https://mlsanity.com/paper/2104.07582/) (2021)
- [Seesaw: High-throughput LLM Inference via Model Re-sharding](https://mlsanity.com/paper/2503.06433/) (2025)
- [Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology](https://mlsanity.com/paper/2308.00846/) (2023)
- [Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization](https://mlsanity.com/paper/2503.18599/) (2025)
- [Benchmarking Memory-Centric Computing Systems: Analysis of Real Processing-in-Memory Hardware](https://mlsanity.com/paper/2110.01709/) (2021)

## Abstract (arXiv)

High-performance Host processors can integrate Processing-In-Memory (PIM) devices, which can accelerate memory-intensive kernels of Machine Learning (ML) models, including Large Language Models (LLMs), by leveraging the large memory bandwidth available at PIM cores. However, Host processor needs consecutive elements distributed across DRAM banks, while PIM cores need consecutive elements within their local banks. This necessitates data rearrangements in ML kernel execution that pose significant performance and programmability challenges, further exacerbated by the need to support diverse PIM devices. Current compilation approaches lack systematic optimization for diverse ML kernels and multiple PIM devices, and may largely ignore data rearrangement costs during the compute code optimization step. We show that data rearrangements and compute code optimization are interdependent, and need to be jointly optimized during the tuning process. Therefore, we design DCC, the first data-centric ML compiler for PIM systems that jointly co-optimizes data rearrangements and compute code in a unified tuning process. DCC integrates a multi-layer PIM abstraction to support multiple PIM backends. DCC enables effective co-optimization of data partitioning strategies with compute loop partitioning schemes. DCC applies PIM-specific code optimizations, and leverages a fast and accurate performance prediction model to select the bestperforming code schedule for a given kernel on a target PIM architecture. Our evaluations in various individual ML kernels show that DCC achieves up to 7.68x speedup (2.21x average) on HBM-PIM, and up to 13.17x speedup (3.92x average) on AttAcc PIM, over GPU-only execution. In end-to-end LLM inference, DCC on AttAcc accelerates GPT-3 and LLaMA-2 by 4.52x average (up to 7.71x in LLaMA-2) over GPU. DCC is open-sourced at https://github.com/SPIN-Research-Group/DCC.