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PetPS: Supporting Huge Embedding Models with Tiered Memory

Embedding models are effective for learning high-dimensional sparse data in the scenarios of ad/recommendation/searching. Traditionally, they are deployed in DRAM parameter servers (PS) for online inference access. However, the ever-increasing model capacity makes this practice suffer from both high storage costs and long recovery time. Rapidly developing Persistent Memory (PM) offers new opportunities to PSs owing to its large capacity at low costs, as well as its persistence, while the application of PM also faces performance challenges.

This project introduce PetPS (Persistent Embedding Table Parameter Server), a low-cost and high-performance PS on PM. It contains two main techniques:

  • PetHash, a PM hash index tailored for embedding model workloads.
  • NIC Gathering, which retrofits the DMA engine on NICs for gathering parameters.

For more details, please refer to our paper.

[VLDB'23] PetPS: Supporting Huge Embedding Models with Persistent Memory

If you find this repository useful, we would appreciate citations to our paper.

System Requirements

  • Mellanox ConnectX-5 NICs and above
  • Intel Optane Persistent Memory (Gen1 or Gen2)
  • RDMA Driver: MLNX_OFED_LINUX-4.9-5.1.0.0 (If you use MLNX_OFED_LINUX-5**, you should modify codes to resolve interface incompatibility)
  • Memcached (to exchange QP information)
  • Software dependencies:
    • Folly v2022.01.17.00
    • GLog v0.5.0
    • fmt
    • PB v3.10.0
    • Intel OneAPI (libtbb v2021.6.0)

Directory structure

petps
|---- benchmark        # evaluation scripts
|---- src              
    |---- base         # basic utilities used for programming
    |---- benchmark    # perf PS clients
    |---- kv_engine    # different kv engines (Dash, PetHash, etc.)
    |---- memory       # simple memory allocators and epoch-based reclaimnation
    |---- pet_kv       # implementation of PetHash
    |---- ps           # parameter server (including the implementation of NIC gathering technique)
|---- test             # unit tests
|---- tools            # small scripts

Setup

Step 1: Setup Dependences (on Ubuntu 18.04)

  • We provide script for installing 3rd-party dependencies, but it is tested only on ubuntu 18.04.

    bash tools/install-dependences.sh

  • For other distributions of linux, you may manually install the dependencies as shown in install-dependences.sh.

Step 2: Setup Persistent Memory

  • Set Optane DCPMM to AppDirect mode.

    $ sudo ipmctl create -f -goal persistentmemorytype=appdirect

  • Switch the mode of PM between fsdax and devdax.

    • RDMA scatter-gather DMA (SG-DMA) requires PM in the devdax mode, while the implementation of some existing PM index (e.g. Dash) requires PM in the fsdax mode.
    • We provide python scripts to switch the mode of PM between fsdax and devdax. Please see the python function InitPMToFsDax and InitPMToDevDax in benchmark/exp_config.py.
    • Don't worry, our script will automatically call the two functions aforementioned for each experiment :).

Step 3: Setup RDMA NIC

  • If you use RoCE and the MTU of your NIC is not equal to 4200 (check with ifconfig), modify the value path_mtu in third_party/Mayfly-main/src/rdma/StateTrans.cpp.

Getting Started

  • Compile the project.

    mkdir build; cd build; cmake .. -DCMAKE_BUILD_TYPE=Release; make -j

  • Set the IPs of servers for evaluation in benchmark/exp_config.py.

  • Set the IP and port of memcached server in third_party/Mayfly-main/memcached.conf.

  • Set the variable exp_lists in benchmark/bench.py, to the experiments you want to run (all experiments in the VLDB paper can be seen in exp_config.py).

  • Run the one-click startup script to replicate our experiments.

    cd benchmark; python3 bench.py

  • You can find the log output in benchmark/log.