This repository contains the source code for ACM ASPLOS 2023.

Yongju Song, Wook-Hee Kim, Sumit Kumar Monga, Changwoo Min, and Young Ik Eom, "Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices," In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS ’23), March 25–29, 2023, Vancouver, BC, Canada.

• PMDK: https://github.com/pmem/pmdk
• liburing: https://github.com/axboe/liburing
• Install other packages

sudo dnf -y update
sudo dnf -y install ndctl ipmctl
dnf search pmem
sudo dnf -y install libpmem-devel librpmem-devel libpmemblk-devel libpmemlog-devel libpmemobj-devel libpmemobj++-devel libpmempool-devel
sudo dnf -y install zlib-devel libatomic autoconf numactl-devel jemalloc-devel gtest-devel tbb-devel boost-devel gperftools
sudo dnf -y install liburing liburing-devel papi-devel automake

The artifact was tested in a machine with following specifications:

• Fedora 29 or higher
• Linux kernel v5.14.18.
• two 20-core Intel(R) Xeon(R) Gold 5218R CPU @ 2.10GHz
• 6 x 16 GB DRAM per socket
• 6 x 128 GB NVM per socket
• 8 x 1 TB Samsung 980 PRO SSDs (with two NVMe RAID Controller HighPoint SSD7103)
• x86-64 CPU supporting AVX512 instructions

#You can check using the following command:
$cat /proc/cpuinfo | egrep -ho 'avx[^ ]*' | sort -u

sudo ipmctl create -goal PersistentMemoryType=AppDirect
sudo ndctl create-namespace -f -e namespace0.0 --mode=fsdax
sudo ndctl create-namespace -f -e namespace1.0 --mode=fsdax
sudo ndctl list --human
[
  {
    "dev":"namespace1.0",
    "mode":"fsdax",
    "map":"dev",
    "size":"744.19 GiB (799.06 GB)",
    "uuid":"cd39c10e-4fb2-4eb7-8dbf-61337a8a8049",
    "sector_size":512,
    "align":2097152,
    "blockdev":"pmem1"
  },
  {
    "dev":"namespace0.0",
    "mode":"fsdax",
    "map":"dev",
    "size":"744.19 GiB (799.06 GB)",
    "uuid":"acf03367-9787-4168-9f6c-e7831d31acdf",
    "sector_size":512,
    "align":2097152,
    "blockdev":"pmem0"
  }
]

For details, see Intel Optane DC Persistent Memory Quick Start Guide

If your hardware device environment is different from the above environment,
you need to modify the file prepare_devices.sh.

$bash prepare_devices.sh
$df -Th
/dev/pmem0                     ext4      733G   73M  696G   1% /mnt/pmem0
/dev/pmem1                     ext4      733G   73M  696G   1% /mnt/pmem1
/dev/nvme0n1                   xfs       932G  6.6G  925G   1% /mnt/hpt0
/dev/nvme1n1                   xfs       932G  6.6G  925G   1% /mnt/hpt1
/dev/nvme2n1                   xfs       932G  6.6G  925G   1% /mnt/hpt2
/dev/nvme3n1                   xfs       932G  6.6G  925G   1% /mnt/hpt3
/dev/nvme5n1                   xfs       932G  6.6G  925G   1% /mnt/hpt4
/dev/nvme6n1                   xfs       932G  6.6G  925G   1% /mnt/hpt5
/dev/nvme7n1                   xfs       932G  6.6G  925G   1% /mnt/hpt6
/dev/nvme8n1                   xfs       932G  6.6G  925G   1% /mnt/hpt7

Compile the Prism source and its benchmark tools

./build.sh

1. Configure Workload Parameters
   • Distribution type: Zipfian, Uniform
   • Workload type: A, B, C, D, E
   • Workload dir
   • Number of Key-value items
   • Zipfian coefficient

vim ./ycsb_generator/generator.sh

2. Generate YCSB Workloads

bash ./ycsb_generator/generator.sh

The generated workload files will be in "Workload dir"

Basic Test

./workload a zipf 32

Test Prism with various configurations.

./run_bench.sh

To learn more about Prism configuration, please refer to PRISM/include/mts-config.h

• If your system resources (e.g., CPU, NVMs, and SSDs) are different from the paper,
  the experimental results may also be different.
• The detailed source code of the Prism components is in directory PRISM/src/.
  • MTS.cpp: Prism's operations
  • KeyIndex.h: Persistent Key Index on NVM
  • AddressTable.* : Heterogeneous Storage Indirection Table (HSIT) on NVM
  • OpLog.*: Persistent Write Buffer (PWB) on NVM
  • ValueStorage.*: Value Storage on Flash SSD
  • CacheThread.*: Scan-aware Value Cache (SVC) on DRAM
  • AIO.*: Opportunistic Thread Combining for Optimized Read

• Yongju Song (Sungkyunkwan University) [email protected]
• Wook-Hee Kim (Konkuk University) [email protected]
• Sumit Kumar Monga (Virginia Tech) [email protected]
• Changwoo Min (Virginia Tech) [email protected]
• Young Ik Eom (Sungkyunkwan University) [email protected]