Artifact for "IDT: Intelligent Data Placement for Multi-tiered Main Memory with Reinforcement Learning", HPDC 2024.

This repository contains source code and experiment scripts for the paper presented in the HPDC 2024 by J. Chang et al.

Contents

This repository contains the RL model, Linux kernel, and experiment scripts. Please refer to the README.md in each subdirectory for more details.

• IDT-Userspace/: RL model for autotuning demotion policy.
• linux/: Linux kernel for region-granularity memory access monitoring and supporting demotion/promotion. The provided linux kernel should be built and installed.
• experiments/: Experiment scripts used in the paper.

The presentation slide for HPDC 2024 is in [HPDC'24] IDT-Slide.pdf.

Hardware prerequisites

We have tested on:

CPU

24-core Intel Xeon Platinum 8260 processor for each socket

Memory

For each socket

• 32GB DDR4 DRAM (fast memory)
• 256GB Intel Optane DCPMM (slow memory), App-direct mode

Contact

Juneseo Chang (jschang0215@snu.ac.kr)