PIM-CACHE: High-Efficiency Content-Aware Copy for Processing-In-Memory

📅 2026-03-24
📈 Citations: 0
✨ Influential: 0
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🤖 AI Summary
This work addresses the performance bottleneck in Processing-in-Memory (PIM) architectures caused by redundant, coarse-grained data transfers between the host and DPUs. To mitigate this inefficiency, the authors propose PIM-CACHE, a lightweight, content-aware data staging layer that introduces content-aware mechanisms into PIM data movement for the first time. By dynamically identifying workload similarity at runtime, PIM-CACHE eliminates redundant data copies and optimizes cache management. Implemented on the UPMEM PIM platform and evaluated using both synthetic and real-world genomic datasets, the approach significantly reduces data transfer overhead and enhances overall system efficiency.

Technology Category

Machine Learning: Hardware-aware MLData Mining & Knowledge Management: Scalability, Parallel & Distributed SystemsConstraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applicationsSecurity and Privacy: Data transparency and provenanceWeb Mining and Content Analysis: Web data generation and simulation
📝 Abstract
Processing-in-memory (PIM) architectures bring computation closer to data, reducing the processor-memory transfer bottleneck in traditional processor-centric designs. Novel hardware solutions, such as UPMEM's in-memory processing technology, achieve this by integrating low-power DRAM processing units (DPUs) into memory DIMMs, enabling massive parallelism and improved memory bandwidth. However, paradoxically, these PIM architectures introduce mandatory coarse-grained data transfers between host DRAM and DPUs, which often become the new bottleneck. We present PIM-CACHE, a lightweight data staging layer that dynamically eliminates redundant data transfers to PIM DPUs by exploiting workload similarity, achieving content-aware copy (CAC). We evaluate PIM-CACHE on both synthetic workloads and real-world genome datasets, demonstrating its effectiveness in reducing PIM data transfer overhead.
Problem

Research questions and friction points this paper is trying to address.

Processing-in-Memory
data transfer bottleneck
coarse-grained data transfers
PIM architectures
memory bandwidth
Innovation

Methods, ideas, or system contributions that make the work stand out.

Processing-in-Memory
Content-Aware Copy
Data Transfer Optimization
PIM-CACHE
Workload Similarity
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