➀ Discusses the limitations of using RDMA in ScaleUP networks, highlighting issues with latency, CPU overload, and chip area constraints. ➁ Explores alternative protocols suitable for ScaleUP, emphasizing the benefits of Ethernet and direct memory access. ➂ Analyzes the challenges and solutions in implementing efficient interconnects for AI accelerators, focusing on network convergence and memory semantics.
Related Articles
- Nvidia Is About To Beat Estimates Again8 months ago
- IBM's New Chip Outperforms GPUs9 months ago
- AI Network Background: Why, What & How of RDMA9 months ago
- AMD Pensando Pollara 400 UltraEthernet RDMA NIC Launched9 months ago
- SiFive launches XM Series for accelerating AI workloads10 months ago
- 9% CAGR 2024-29 for advanced substrate market, says Yole10 months ago
- AI Accelerator Interconnect and Cloud AI Processors: Tesla Shines11 months ago
- RDMA Technology in Large-Scale Model Training11 months ago
- The Industry’s Lowest-Power 16-Lane Retimerabout 1 year ago