Legend: A Lightweight Heterogeneous System for Out-of-core Graph Embedding Learning

Legend is a lightweight heterogeneous system for efficient and cost-effective graph embedding learning, comprising a CPU, a GPU and an NVMe SSD. It adopts a novel workflow that reconsiders data placement and meticulously assigning tasks to leverage the unique strengths of each hardware component. A prefetch-friendly order is proposed to support embedding prefetching from NVMe SSD to GPU. Furthermore, it also optimize GPU-NVMe SSD direct access and GPU computing to achieve better performance.

Environment preparation

Requiements

• Python 3.8
• CUDA 11.1
• torch 1.7.1
• Samsung 980 NVMe SSD
• Nvidia A100 GPU

Compiling Nvidia Driver

The Nvidia driver kernel sources are typically installed in /usr/src/. Following commands are used to get the kernel symbols.

$ cd /usr/src/nvidia-550.54.15
$ sudo make

Unbinding NVMe Driver

The default NVMe driver should be unbind first before install the customized NVMe driver. The PCI ID of the NVMe SSD is required to do this, which can be find by using lspci. We assume the PCI ID is 86:00.0. The NVMe driver can be unbinded using following commands.

$ echo -n "0000:86:00.0" > /sys/bus/pci/devices/0000\:86\:00.0/driver/unbind

Project Building

From the project root directory, do the following:

$ mkdir build; cd build
$ cmake
$ make libnvm
$ make benchmarks

After this, the libnvm kernel module have to be compiled. In the build directory, do the following:

$ cd module
$ make

Subsequently, we need to load the custom libnvm kernel module in the module directory. It can be loaded and unloaded with the following:

$ sudo make load
$ sudo make unload

This should create a /dev/libnvm0 device file, representing the disk's BAR0.

Datasets

Each dataset can be obtained from the following links.

Dataset	Nodes	Edges	Relations	Link
FB15k	15k	592k	1345	https://dl.fbaipublicfiles.com/starspace/fb15k.tgz
LiveJournal	4.8M	68M	-	https://snap.stanford.edu/data/soc-LiveJournal1.txt.gz
Twitter	41.6M	1.46B	-	https://snap.stanford.edu/data/twitter-2010.txt.gz
Freebase86m	86.1M	304.7M	14824	https://data.dgl.ai/dataset/Freebase.zip

Example Running

The executable files are located in build/bin

To train without NVMe SSD, we can run the code with the followings commands.

$ ./nvm-train-nonvme

The dataset used to train should be modified in the code ./benchmarks/train_nonvme/main.cu.

To train with NVMe SSD, we can run the code with the followings commands.

$ ./nvm-train-nvme --ctrl=/dev/libnvm0 --threads=4096 --page_size=32768 --queue_pairs=8 --queue_depth=1024

• ctrl is the path of the custom NVMe controller file.
• threads specifies the threads used to load data from NVMe SSD.
• page_size denotes the size of each IO request.
• queue_pairs indicates the number of queues in the NVMe controller.
• queue_depth specifies the queue depth per queue.

The dataset used to train should be modified in the code ./benchmarks/train_nvme/main.cu.

Acknowledgments

The GPU-SSD direct access module of this project is built on top of an open-source codebase available here. We employ the framework of this codebase and develop a customized queue management mechanism (submission queue inserting, doorbell ringing, and completion queue polling) to improve the throughput between GPU and SSD.