ParCIS / Ok-TopkLinks
Ok-Topk is a scheme for distributed training with sparse gradients. Ok-Topk integrates a novel sparse allreduce algorithm (less than 6k communication volume which is asymptotically optimal) with the decentralized parallel Stochastic Gradient Descent (SGD) optimizer, and its convergence is proved theoretically and empirically.
☆26Updated 2 years ago
Alternatives and similar repositories for Ok-Topk
Users that are interested in Ok-Topk are comparing it to the libraries listed below
Sorting:
- Hi-Speed DNN Training with Espresso: Unleashing the Full Potential of Gradient Compression with Near-Optimal Usage Strategies (EuroSys '2…☆15Updated last year
- A Cluster-Wide Model Manager to Accelerate DNN Training via Automated Training Warmup☆35Updated 2 years ago
- ☆14Updated 3 years ago
- ☆20Updated 3 years ago
- ☆10Updated 2 years ago
- Artifacts for our ASPLOS'23 paper ElasticFlow☆52Updated last year
- Artifacts for our SIGCOMM'22 paper Muri☆43Updated last year
- Artifact for OSDI'23: MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Mult…☆41Updated last year
- Artifact for PPoPP22 QGTC: Accelerating Quantized GNN via GPU Tensor Core.☆30Updated 3 years ago
- ☆24Updated 3 years ago
- ☆25Updated 2 years ago
- ☆37Updated 2 months ago
- DISB is a new DNN inference serving benchmark with diverse workloads and models, as well as real-world traces.☆54Updated last year
- ☆17Updated 2 years ago
- Tacker: Tensor-CUDA Core Kernel Fusion for Improving the GPU Utilization while Ensuring QoS☆31Updated 7 months ago
- ☆31Updated last year
- ☆51Updated 2 years ago
- LLM serving cluster simulator☆110Updated last year
- AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving (OSDI 23)☆85Updated 2 years ago
- Compiler for Dynamic Neural Networks☆46Updated last year
- ☆82Updated 2 years ago
- SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training☆35Updated 2 years ago
- ☆56Updated 4 years ago
- PipeSwitch: Fast Pipelined Context Switching for Deep Learning Applications☆127Updated 3 years ago
- Model-less Inference Serving☆92Updated last year
- ☆15Updated 3 years ago
- ☆40Updated 4 years ago
- Proteus: A High-Throughput Inference-Serving System with Accuracy Scaling☆13Updated last year
- A GPU-accelerated DNN inference serving system that supports instant kernel preemption and biased concurrent execution in GPU scheduling.☆43Updated 3 years ago
- Multi-Instance-GPU profiling tool☆59Updated 2 years ago