Lune

MICRO2024顶会

Low-Overhead General-Purpose Near-Data Processing in CXL Memory Expanders

Hyungkyu Ham, Jeongmin Hong, Geonwoo Park, Yunseon Shin, Okkyun Woo, Wonhyuk Yang, Jinhoon Bae, Eunhyeok Park, Hyojin Sung, Euicheol Lim, Gwangsun Kim

2024年份
26被引次数
4顶会引用

摘要

Emerging Compute Express Link (CXL) enables cost-efficient memory expansion beyond the local DRAM of processors. While its CXL.mem protocol provides minimal latency overhead through an optimized protocol stack, frequent CXL memory accesses can result in significant slowdowns for memory-bound applications whether they are latency-sensitive or bandwidth-intensive. The near-data processing (NDP) in the CXL controller promises to overcome such limitations of passive CXL memory. However, prior work on NDP in CXL memory proposes application-specific units that are not suitable for practical CXL memory-based systems that should support various applications. On the other hand, existing CPU or GPU cores are not costeffective for NDP because they are not optimized for memorybound applications. In addition, the communication between the host processor and CXL controller for NDP offloading should achieve low latency, but existing CXL.io/PCIe-based mechanisms incur µs-scale latency and are not suitable for fine-grained NDP.

To achieve high-performance NDP end-to-end, we propose a low-overhead general-purpose NDP architecture for CXL memory referred to as Memory-Mapped NDP (M 2 NDP), which comprises memory-mapped functions (M 2 func) and memory-mapped µthreading (M 2 µthread). M 2 func is a CXL.mem-compatible lowoverhead communication mechanism between the host processor and NDP controller in CXL memory. M 2 µthread enables lowcost, general-purpose NDP unit design by introducing lightweight µthreads that support highly concurrent execution of kernels with minimal resource wastage. Combining them, M 2 NDP achieves significant speedups for various workloads by up to 128x (14.5x overall) and reduces energy by up to 87.9% (80.3% overall) compared to baseline CPU/GPU hosts with passive CXL memory. The M

问问这篇 Paper

智能体会读完全文。

Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。

可以从这些问题问起

智能体调用

Luneget_paper_fulltext

在 Lune 里问

免费开始,无需绑卡

lune papers fulltext c2de036f-d2b9-477e-8b86-a4be52b59c72

引用它的顶会 Paper4

问问它们各自怎么用它

它引用的顶会 Paper25

相关 Paper

黄昏的海面,两侧是细线勾勒的悬崖