nccl, RaBitQ, and triSYCL

PyTorchConference2025_GithubRepos.json

@@ -1104,6 +1104,7 @@
   {
     "repo_name": "nccl",
     "repo_link": "https://github.com/NVIDIA/nccl",
+    "category": "distributed computing",
     "github_about_section": "Optimized primitives for collective multi-GPU communication",
     "homepage_link": "https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html",
     "contributors_all": 51,
@@ -1233,6 +1234,7 @@
   {
     "repo_name": "RaBitQ",
     "repo_link": "https://github.com/gaoj0017/RaBitQ",
+    "category": "quantization",
     "github_about_section": "[SIGMOD 2024] RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search",
     "homepage_link": "https://github.com/VectorDB-NTU/RaBitQ-Library",
     "github_topic_closest_fit": "nearest-neighbor-search",
@@ -1287,6 +1289,7 @@
   {
     "repo_name": "triSYCL",
     "repo_link": "https://github.com/triSYCL/triSYCL",
+    "category": "parallel computing",
     "github_about_section": "Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group",
     "homepage_link": "https://trisycl.github.io/triSYCL/Doxygen/triSYCL/html/index.html",
     "github_topic_closest_fit": "parallel-programming",