4 ms·

> I believe you are not guaranteed for the edge data of adjacent nodes to be adjacent in memory The edge data of a particular node is contiguous, but yes, the

by pgera 3y ago

> I believe you are not guaranteed for the edge data of adjacent nodes to be adjacent in memory The edge data of a particular node is contiguous, but yes, the edge data of a collection of nodes is not contiguous. You can reorder (permute) the graph for some metric as a preprocessing step so that you get better locality for your average access pattern. This only works for static graphs though. > For float-based edge data I think quantization works well, and I believe you can further compress the ROW/COL indices Yes, index compression is pretty well studied and understood. The challenge here is mostly good compression ratio and high decompression performance. There are a couple of works that I'm aware of that do this for gpus. This repo by Mo Sha et al. (https://github.com/desert0616/GCGT https://github.com/desert0616/GCGT) is pretty good, and I also did some work in this space (https://github.com/pgera/efg https://github.com/pgera/efg).

WinLychee 3y ago
Awesome, thanks! Hacking on something in this space atm ;D
WinLychee 3y ago
Oh one more question (checked out your repo): why the usage of GPUs here vs CPUs? The possibility of more parallelism when traversing the graph versus CPU?
- pgera 3y ago
  Yes, you can get good performance on GPUs due to the parallelism and memory bandwidth. On a new GPU like H100, I believe you can do ~ 50-100 GTEPS (billions of traversed edges per sec) in a BFS. I'm not sure where the state of the art on CPUs is at, but you can certainly do efficient implementations on CPUs too. This paper is a few years old (https://people.csail.mit.edu/jshun/spaa2018.pdf https://people.csail.mit.edu/jshun/spaa2018.pdf), but has some numbers. For CPU based index compression/decompression, the webgraph framework is quite mature and widely used and there's also Ligra+ that does it.
  - WinLychee 3y ago
    Very cool, will digest all this! For my use-case CPU + lots of RAM has been fast enough, and there's a balance between per-thread latency and throughput (serving data concurrently). I'm definitely interested in compressing down the graph data further to see if I can drop latency further, will see if I can adapt some of this. Super neat to see this on GPUs, will check that out too. Also a fan of https://github.com/frankmcsherry/COST https://github.com/frankmcsherry/COST if you've seen it before as well!

by pgera 3y ago

WinLychee 3y ago

Awesome, thanks! Hacking on something in this space atm ;D

WinLychee 3y ago

Oh one more question (checked out your repo): why the usage of GPUs here vs CPUs? The possibility of more parallelism when traversing the graph versus CPU?

pgera 3y ago
Yes, you can get good performance on GPUs due to the parallelism and memory bandwidth. On a new GPU like H100, I believe you can do ~ 50-100 GTEPS (billions of traversed edges per sec) in a BFS. I'm not sure where the state of the art on CPUs is at, but you can certainly do efficient implementations on CPUs too. This paper is a few years old (https://people.csail.mit.edu/jshun/spaa2018.pdf https://people.csail.mit.edu/jshun/spaa2018.pdf), but has some numbers. For CPU based index compression/decompression, the webgraph framework is quite mature and widely used and there's also Ligra+ that does it.
- WinLychee 3y ago
  Very cool, will digest all this! For my use-case CPU + lots of RAM has been fast enough, and there's a balance between per-thread latency and throughput (serving data concurrently). I'm definitely interested in compressing down the graph data further to see if I can drop latency further, will see if I can adapt some of this. Super neat to see this on GPUs, will check that out too. Also a fan of https://github.com/frankmcsherry/COST https://github.com/frankmcsherry/COST if you've seen it before as well!