implementation detail · filed under optimization
Minimal recomputation-graph extraction by backward traversal
Finding the minimal subgraph needed to recompute an annotated tensor by traversing the graph backward.
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Evidence
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For each annotated tensor, it performs a backward traversal to identify the minimal subgraph required for its recomputation.
usedsoftware implementationin DeepSeek-V4DeepSeek
Filed alongside
Other methods under optimization :: training runtime.
Offline checkpoint mergingAsynchronous checkpointingAsymmetric local-read and single-writer cache pathsAutotune configuration generationBatch-level embedding prefetchCaching the distributed checkpoint save planComposable activation storage policiesCPU-resident optimizer statesCross-rank remote activation offloadingDouble-buffered chunked streaming of reference-model weightsElement-wise activation recomputationGPU-to-GPU weight synchronization over GPUDirect RDMAHeartbeat-driven fault toleranceIn-flight recovery systemIn-flight weight updatesJob-level eviction and reclaimOverlapping NCCL transfers with device-to-host copiesPersistent checkpoint worker processesPersistent shared-storage cache for compiled artifactsPolicy-model gradient-buffer reuse for reference-model weightsPost-iteration NVMe offloading of training statesPre-admission hardware stress testingSame-node sticky pod respawnSeeding node-local storage from a warm shared cache