CRAC: Checkpoint-Restart Architecture for CUDA with Streams and UVM
Not on the map. Why?
Abstract (arXiv)
The share of the top 500 supercomputers with NVIDIA GPUs is now over 25% and continues to grow. While fault tolerance is a critical issue for supercomputing, there does not currently exist an efficient, scalable solution for CUDA applications on NVIDIA GPUs. CRAC (Checkpoint-Restart Architecture for CUDA) is new checkpoint-restart solution for fault tolerance that supports the full range of CUDA applications. CRAC combines: low runtime overhead (approximately 1% or less); fast checkpoint-restart; support for scalable CUDA streams (for efficient usage of all of the thousands of GPU cores); and support for the full features of Unified Virtual Memory (eliminating the programmer's burden of migrating memory between device and host). CRAC achieves its flexible architecture by segregating application code (checkpointed) and its external GPU communication via non-reentrant CUDA libraries (not checkpointed) within a single process's memory. This eliminates the high overhead of inter-process communication in earlier approaches, and has fewer limitations.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Aug 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 2.
4 papers.
- 2022RARE: Renewable Energy Aware Resource Management in Datacenterscited by 1
- 2023Transactional Python for Durable Machine Learning: Vision, Challenges, and Feasibilitycited by 1
- 2026Foundry: Template-Based CUDA Graph Context Materialization for Fast LLM Serving Cold Startcited by 1
- 2026Fast Recovery for LLM Serving via Decoupled Device Memory Lifetime in Dynamocited by 0
- 2026Fast Recovery for LLM Serving via Decoupled Device Memory Lifetime in Dynamo21 Sep 2026
- 2026Foundry: Template-Based CUDA Graph Context Materialization for Fast LLM Serving Cold Start8 Apr 2026
- 2023Transactional Python for Durable Machine Learning: Vision, Challenges, and Feasibility15 May 2023
- 2022RARE: Renewable Energy Aware Resource Management in Datacenters10 Nov 2022