TRANSIT is a transparent scale-in framework to enable multi-node model training on fewer GPUs while maintaining training efficiency by transparently leveraging CPU DRAM as an extension of GPU memory during distributed training. It achieves this through a user-space interposition layer, requiring no modifications to the...
Hyungyo Kim, Nicholas Satchanov, Hrishi Shah et al.· 0 citations
LLQM (Low-Level Quantum Machine), a profiling-driven meta-framework for quantum-classical pipelines that decomposes pipelines into fine-grained tasks and continuously profiles their CPU/GPU, memory, QPU, and queue dependencies alongside real-time hardware states, is presented.
Ayush Bansal, Owen Cochell, Santiago Núñez-Corrales et al.· 0 citations
This paper extends the validation of QRMI to a broad range of workload managers, including PBS, LSF, Grid Engine, Kubernetes, and the Flux Framework, encompassing traditional batch schedulers, a cloud-native orchestration platform, and a graph-based scheduler.
Thomas Badts, T. Boyle, Claudio Carvalho et al.· arXiv.org· 4 citations
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