Large Language Models (LLMs) are usually trained with 3D (data, tensor, and pipeline) parallelism—in shared GPU clusters where the available resources are highly dynamic. Rescheduling the idle resources to ongoing jobs can help improve cluster utilization, but doing so for 3D-parallelized training jobs suffers large overheads in performance modeling, decision making, and redeployment. We present Suika, a cluster training system that supports efficient and high-quality resource rescheduling for 3D-parallelized LLM training jobs. Suika holistically addresses the complexity challenges by exploiting the incremental nature of rescheduling. For performance modeling, it builds an accurate performance estimator with non-disruptive online profiling. For decision-making, it employs topology-aware sorting and an expand-and-balance algorithm to reduce the complexity of resource allocation and job parallelization, without compromising decision quality. Suika further integrates a device-to-device redeployment method to leverage the overlapping nature of incremental reconfiguration for overhead reduction. Experiments on 64-GPU physical cluster and 1024-GPU simulated cluster show that, Suika achieves 1.29 ~ 1.31× reduction in average JCT compared to state-of-the-art schedulers.

Efficiently training large-scale models (LMs) in GPU clusters involves two separate avenues: inter-job dynamic scheduling and intra-job adaptive parallelism (AP). However, existing dynamic schedulers struggle with large-model scheduling due to the mismatch between static parallelism (SP)-aware scheduling and AP-based execution, leading to cluster inefficiencies such as degraded throughput and prolonged job queuing. This paper presents Arena, a large-model training system that co-designs dynamic scheduling and adaptive parallelism to achieve high cluster efficiency. To reduce scheduling costs while improving decision quality, Arena designs low-cost, disaggregated profiling and AP-tailored, load-aware performance estimation, while unifying them by sharding the joint scheduling-parallelism optimization space via a grid abstraction. Building on this, Arena dynamically schedules profiled jobs in elasticity and heterogeneity dimensions, and executes them using efficient AP with pruned search space. Evaluated on heterogeneous testbeds and production workloads, Arena reduces job completion time by up to 49.3% and improves cluster throughput by up to 1.6X.

As the foundational component of versatile AI applications, training an multimodal large language model (MLLM) relies on multimodal datasets with dynamic modality mixture proportions and sample length distributions. However, existing MLLM systems remain inefficient under dynamic workloads, due to statically coupled decisions of resource allocation and model parallelization between encoders and the LLM backbone. This paper presents MegaScale-Omni, an industrial-grade MLLM training system tailored for dynamic workload adaption and hyper-scale deployment. MegaScale-Omni is built upon the training scheme of encoder-LLM multiplexing with three key innovations: (1) Decoupled parallelism strategies with long-short sequence parallelism for encoders to process variable-length samples, and full-fledged 5D parallelism for the LLM backbone, both organized under a communication-efficient parallelization layout. (2) Unified encoder-LLM representations for flexible, extensible colocation, and a new paradigm of encoder-LLM joint pipeline with workload resilience. (3) Workload balancing techniques via decentralized grouped reordering in data loaders and adaptive resharding from encoder to LLM ranks. MegaScale-Omni is deployed as the foundation of our in-house large-scale MLLM training tasks with thousands of GPUs. Our experimental results demonstrate 1.27×–7.57× throughput improvement under production-grade dynamic workloads, as compared to four state-of-the-art systems.

Data centers now allow multiple applications that have lightweight workloads to share a GPU. Existing temporal or spatial sharing systems struggle to provide efficient and accurate quota assignments. We observe that the performance of the multi-user system is often underestimated because of the existence of unused GPU "bubbles" and can be enhanced by squeezing the bubbles. Based on this observation, we design Bless, a bubble-less spatial-temporal sharing GPU system that fine-tunes the GPU resource allocation to improve multi-user performance. Bless leverages precise computing resource management and fine-grained kernel scheduling to ensure stringent quota guarantees and reduce latency fairly for applications with varying GPU quotas. We implement and evaluate Bless with multiple applications and workloads. Our result shows that Bless achieves 21.1% - 37.3% average latency reduction over the state-of-the-art while guaranteeing the promised quota for all applications.

Job packing is an effective technique to harvest the idle resources allocated to the deep learning (DL) training jobs but not fully utilized, especially when clusters may experience low utilization, and users may overestimate their resource needs. However, existing job packing techniques tend to be conservative due to the mismatch in scope and granularity between job packing and cluster scheduling. In particular, tapping the potential of job packing in the training cluster requires a local and fine-grained coordination mechanism. To this end, we propose a novel job-packing middleware named Gimbal , which operates between the cluster scheduler and the hardware resources. As middleware, Gimbal must not only facilitate coordination among the packed jobs but also support various scheduling objectives of different schedulers. Gimbal achieves dual functionality by introducing a set of worker calibration primitives designed to calibrate workers’ execution status in a fine-grained manner. The primitives obscure the complexity of the underlying job and resource management mechanisms, thus offering the generality and extensibility for crafting coordination policies tailored to various scheduling objectives. We implement Gimbal on a real-world GPU cluster and evaluate it with a set of representative DL training jobs. The results show that Gimbal improves different scheduling objectives up to 1.32× compared with the state-of-the-art job packing techniques.