Azure / msccl-executor-nccl
☆36Updated 3 months ago
Alternatives and similar repositories for msccl-executor-nccl:
Users that are interested in msccl-executor-nccl are comparing it to the libraries listed below
- NCCL Profiling Kit☆127Updated 8 months ago
- Microsoft Collective Communication Library☆60Updated 3 months ago
- Paella: Low-latency Model Serving with Virtualized GPU Scheduling☆58Updated 10 months ago
- ☆20Updated last month
- ☆87Updated 6 months ago
- nnScaler: Compiling DNN models for Parallel Training☆101Updated last month
- Compare different hardware platforms via the Roofline Model for LLM inference tasks.☆93Updated last year
- Automated Parallelization System and Infrastructure for Multiple Ecosystems☆78Updated 4 months ago
- Thunder Research Group's Collective Communication Library☆33Updated 10 months ago
- FlexFlow Serve: Low-Latency, High-Performance LLM Serving☆29Updated this week
- NCCL Fast Socket is a transport layer plugin to improve NCCL collective communication performance on Google Cloud.☆116Updated last year
- ☆55Updated 2 months ago
- RCCL Performance Benchmark Tests☆60Updated last week
- ☆72Updated 3 years ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆59Updated 2 weeks ago
- A distributed KV store for disaggregated LLM inference☆62Updated this week
- Artifact of OSDI '24 paper, ”Llumnix: Dynamic Scheduling for Large Language Model Serving“☆60Updated 9 months ago
- ☆75Updated 2 years ago
- Synthesizer for optimal collective communication algorithms☆106Updated 11 months ago
- PyTorch distributed training acceleration framework☆44Updated last month
- Official repository for the paper DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines☆17Updated last year
- Stateful LLM Serving☆48Updated last week
- High performance Transformer implementation in C++.☆105Updated 2 months ago
- MSCCL++: A GPU-driven communication stack for scalable AI applications☆311Updated this week
- ☆24Updated this week
- Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines.☆60Updated last year
- ☆45Updated this week
- ☆87Updated last week