@bkj Essentials Related Repos - gunrock/essentials GitHub Wiki
Partial list of projects that @bkj worked on from Nov 2020 - May 2021.
If you have any questions about these projects, please open an issue tagging @bkj.
Documentation
- essentials_guides
- Markdown guides to getting started w/ essentials
Applications
- mgpu_sssp - MultiGPU SSSP implementation, using thrust + NCCL. Equivalent to VN HIVE workload.
- cuda_ppr - CUDA PPR (Parallel PageRank Nibble) implementation, using
thrust
- application_classification
- branch:master - re-worked single GPU implementation of application classification
- branch:dev/mgpu - MGPU implementation using thrust. Performance issues because of blocking
cudaMalloc
andcudaFree
- branch:dev/mgpu_manual_reduce - changes to
dev/mgpu
to remove performance issues by doing manual memory management. A little ugly, so haven't merged todev/mgpu
ormaster
yet.
- graphblas_proj
- branch:dev/mgpu2 -- MGPU implementation of
graph_projections
HIVE workload
- branch:dev/mgpu2 -- MGPU implementation of
Python bindings
- python_essentials - Python wrappers for
essentials
usingpybind11
andpytorch
- Works, but may be difficult to install given version (in)compatabilities between
pytorch
,cudnn
, andcuda
versions
- Works, but may be difficult to install given version (in)compatabilities between
Scratch
- bkj/essentials - workspace for all of my
essentials
experiments- I think most everything interesting here has been merged to
gunrock/essentials
- I think most everything interesting here has been merged to
- mgpu_test
- Scratch repository for experiments w/ MGPU filters in
thrust
- Scratch repository for experiments w/ MGPU filters in
Unstable
- https://github.com/cfld/cugraph
- Fork of
cugraph
showing how to bind toessentials
APIs - Needs to be updated to work w/ current versions of
cugraph
and `essentials
- Fork of
- https://github.com/bkj/async-queue-paper (private -- can give access)
- Scratch repository for async cuda experiments
- https://github.com/cfld/cuda-async-bfs (private -- can give access)
- Minimal implementation of async BFS in CUDA