The race to build a distributed GPU runtime
6 days ago
- #data processing
- #distributed computing
- #GPU acceleration
- GPUs have provided significant speedups in data processing, but data growth now exceeds single GPU server capacity.
- Distributed computing coordinates tasks across datacenters and server clusters to handle large-scale jobs efficiently.
- Data movement between GPUs, CPUs, storage, and networks becomes the bottleneck at datacenter scale.
- NVIDIA and AMD are developing distributed runtimes to optimize data movement and keep GPUs from idling.
- NVIDIA's initiatives include GPU-accelerated Spark, Dask-powered RAPIDS, and CUDA DTX for distributed execution.
- AMD is working on HIP and ROCm-DS to mirror NVIDIA's CUDA-X/RAPIDS ecosystem.
- Voltron Data's Theseus is a distributed runtime optimized for efficient data movement, outperforming competitors in benchmarks.
- Theseus runs on both NVIDIA and AMD ecosystems, providing flexibility and performance for large-scale analytics and AI.