PhoenixOS: Concurrent OS-level GPU Checkpoint and Restore with Validated Speculation
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Abstract (arXiv)
PHOENIXOS (PHOS) is the first OS service that can concurrently checkpoint and restore (C/R) GPU processes--a fundamental capability for critical tasks such as fault tolerance, process migration, and fast startup. While concurrent C/R is well-established on CPUs, it poses unique challenges on GPUs due to their lack of essential features for efficiently tracing concurrent memory reads and writes, such as specific hardware capabilities (e.g., dirty bits) and OS-mediated data paths (e.g., copy-on-write). To ensure correct concurrent C/R, PHOS proactively detects GPU memory reads and writes through a two-step process: first, it speculates about GPU memory accesses based on the arguments used when launching GPU kernels; then, it validates these accesses efficiently at runtime using binary instrumentation. With this validated speculation, PHOS retrofits CPU-based concurrent C/R for GPUs through software-based approaches, including soft copy-on-write, soft recopy, and soft on-demand restore. PHOS further proposes several GPU-aware techniques for efficient GPU C/R, including coordinated checkpoint data transfer and execution context pool. For downstream tasks that use C/R for tolerating failures, migrating processes between machines, and accelerating cold starts in serverless computing, PHOS achieves orders of magnitude higher performance than state-of-the-art OS-level GPU C/R systems like NVIDIA cuda-checkpoint.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2024.
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Citing papers per year: 2025: 2; 2026: 4.
6 papers.
- 2025Laminar: A Scalable Asynchronous RL Post-Training Frameworkcited by 35
- 2025Fast LLM Post-training via Decoupled and Fastest-of-N Speculationcited by 4
- 2026SmartGen: Seamless Disaggregated LLM Inference with Selective KV Cache Transfercited by 2
- 2026Foundry: Template-Based CUDA Graph Context Materialization for Fast LLM Serving Cold Startcited by 1
- 2026Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregationcited by 0
- 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
- 2026SmartGen: Seamless Disaggregated LLM Inference with Selective KV Cache Transfer30 Jul 2026
- 2026Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation11 Apr 2026
- 2026Foundry: Template-Based CUDA Graph Context Materialization for Fast LLM Serving Cold Start8 Apr 2026
- 2025Fast LLM Post-training via Decoupled and Fastest-of-N Speculation20 Nov 2025
- 2025Laminar: A Scalable Asynchronous RL Post-Training Framework14 Oct 2025