The paper introduces COMPASS-ABS, a scheduling framework for shared GPU clusters that reduces resource fragmentation for deep learning training jobs. It defines a new metric, Scheduler-Induced Fragmentation (SIF), which does not rely on historical workload data, and presents the COMPASS algorithm that confines cluster states within an Anchor-Based Space (ABS) to keep fragmentation low. Experiments on both a physical and a simulated cluster show that COMPASS-ABS improves resource utilization and shortens job completion times by mitigating fragmentation.
arXiv:2608. 06441v1 Announce Type: new Abstract: Full-graph GNN training delivers high accuracy but scales poorly on multi-server clusters due to heavy, irregular inter-node embedding exchanges.
arXiv:2402. 09589v2 Announce Type: replace-cross Abstract: We present MLCC, a novel technique to augment today's congestion control algorithms to accelerate DNN training jobs in shared GPU clusters in a fully distributed manner.
arXiv:2608. 15762v1 Announce Type: cross Abstract: Container-granularity scheduling leaves abundant short-lived idle slices within containers unexploited.
arXiv:2606. 29518v1 Announce Type: cross Abstract: With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge.
arXiv:2512. 10236v2 Announce Type: replace-cross Abstract: Modern ML workloads demand distributing training and inference across multiple GPUs.
arXiv:2609.14968v1 Announce Type: new Abstract: Online scheduling of dependency-aware tasks in heterogeneous cloud clusters is a fundamental yet challenging problem due to the complex interplay betwe...
arXiv:2609.39481v1 Announce Type: cross Abstract: Efficient resource provisioning for large-scale workflows on cloud infrastructures is a critical performance engineering challenge. These workflows a...
arXiv:2605. 23247v2 Announce Type: replace Abstract: In this paper, we introduce the first machine learning framework for predicting optimal processing times in Single-Level Tree Network (SLTN) architectures for the Divisible Load Theory (DLT) paradigm.
arXiv:2606. 01007v1 Announce Type: cross Abstract: Sparsely activated Mixture-of-Experts (MoE) models scale capacity via conditional computation, but distributed inference suffers from cross-GPU expert communication and routing-induced load imbalance.
arXiv:2606. 01680v1 Announce Type: cross Abstract: Network failures are among the most frequent hardware faults in large-scale GPU clusters and a leading cause of training-job interruptions.
The paper introduces Global Clustered Parallel Split Learning (GCPSL), which partitions clients into fixed clusters and runs Parallel Split Learning with Global Sampling (GPSL) concurrently across these clusters, periodically merging client and server model segments. Experiments with 256 logical clients show that increasing the number of concurrent workloads boosts direct data participation, though smaller clusters may slightly reduce accuracy. On a four‑GPU setup, label‑aware GCPSL achieves 85% CIFAR‑10 validation accuracy in about 6.13 minutes, compared to 19.09 minutes for serialized workloads, and size‑balanced cluster assignments improve participation by 3.25 percentage points.
arXiv:2604. 23841v2 Announce Type: replace-cross Abstract: Efficiently solving the Job Shop Scheduling Problem in real-world industrial applications requires policies that are both computationally lean and topologically robust.