3 ms·

This is great! How does it compare to blazegraph and how does it compare to redisgraph? As far as I can tell, it's not for doing parallel graph analysis (i.e.

This is great! How does it compare to blazegraph and how does it compare to redisgraph? As far as I can tell, it's not for doing parallel graph analysis (i.e. GAS) on the GPU, is that right? (It's hard to tell, because I'm also seeing that this is designed to be used for GNNs.) But if I wanted to spawn lots of workers on a single machine, I could do that?

wenyuanyu 6y ago
> How does it compare to blazegraph and how does it compare to redisgraph? GraphScope is not a graph database, and it cannot handle updates to graphs. The graphs are loaded to memory and are "immutable", but queries on GraphScope are typically faster than graph databases, and GraphScope scales very well - you can launch a larger session from your python notebook to handle a bigger graph or run a complex algorithm. > As far as I can tell, it's not for doing parallel graph analysis (i.e. GAS) on the GPU, is that right? (It's hard to tell, because I'm also seeing that this is designed to be used for GNNs.) But if I wanted to spawn lots of workers on a single machine, I could do that? Internally, we have an early version running with GPU support. However,it does not really seem cost-effective to running algorithms with lots of random memory accesses on sparse data structures like large graphs on GPU. There could be ~10x speed-up, but because memory on GPUs are typically smaller than main memory, that means the graphs would not be too slow to be processed on CPUs either. For GNN part, we focused on the sampling, it is currently done on CPUs too. After the samples get feeding to TensorFlow, TensorFlow could utilize GPU depend on the setting.
- macawfish 6y ago
  Thank you, this is really helpful. I think clarifying that those limitations somewhere early on in the website could save people a lot of time in figuring out whether this is a good choice for them. It sounds like this might not be best for realtime graph analytics, but would be great for very large static graphs.
  - wenyuanyu 6y ago
    Stay tuned. We have a plan to build a graph storage engine that can work side by side with GraphScope.

wenyuanyu 6y ago

> How does it compare to blazegraph and how does it compare to redisgraph? GraphScope is not a graph database, and it cannot handle updates to graphs. The graphs are loaded to memory and are "immutable", but queries on GraphScope are typically faster than graph databases, and GraphScope scales very well - you can launch a larger session from your python notebook to handle a bigger graph or run a complex algorithm. > As far as I can tell, it's not for doing parallel graph analysis (i.e. GAS) on the GPU, is that right? (It's hard to tell, because I'm also seeing that this is designed to be used for GNNs.) But if I wanted to spawn lots of workers on a single machine, I could do that? Internally, we have an early version running with GPU support. However,it does not really seem cost-effective to running algorithms with lots of random memory accesses on sparse data structures like large graphs on GPU. There could be ~10x speed-up, but because memory on GPUs are typically smaller than main memory, that means the graphs would not be too slow to be processed on CPUs either. For GNN part, we focused on the sampling, it is currently done on CPUs too. After the samples get feeding to TensorFlow, TensorFlow could utilize GPU depend on the setting.

macawfish 6y ago
Thank you, this is really helpful. I think clarifying that those limitations somewhere early on in the website could save people a lot of time in figuring out whether this is a good choice for them. It sounds like this might not be best for realtime graph analytics, but would be great for very large static graphs.
- wenyuanyu 6y ago
  Stay tuned. We have a plan to build a graph storage engine that can work side by side with GraphScope.