2026· Euromicro Conference on Real-Time Systems· pp. 16:1-16:24· 0 citations· 57 references
Computer Science
TL;DR
DySM is a closed-loop response time control algorithm for spatially shared GPUs in soft real-time systems designed using feedback control theory for guaranteed system stability and control accuracy and outperforms state-of-the-art solutions by providing runtime real-time guarantees.
This work introduces CTA-pipelining, an execution paradigm designed to exploit shared-memory multi-GPU systems and demonstrates its capability using CUTLASS, cuBLAS, and NCCL libraries on 8-GPU H200 and B200 systems.
Tingkai Liu, Muralidhar Andoorveedu, Sanjoy Das et al.· 0 citations
DEFT is presented, an energy-aware scheduling framework that jointly optimizes task-to-device assignment and per-GPU DVFS configuration for task-based multi-GPU applications and employs a cost-model–driven strategy that integrates slack awareness, throughput awareness, and explicit modeling of task execution cost, inter-GPU data movement, and DVFS transition overheads, enabling coordinated placement and frequency decisions under dynamic runtime conditions.
Jing Chen, Miquel Pericàs· International Conference on...· 0 citations
Current GPU Collective Communication Libraries (CCLs) employ predefined schedules optimized for stable environments. Their supported schedules and selection logic are fixed at communicator initialization, which fails to account for evolving runtime conditions, such as workload characteristics and hardware health status. Consequently, long-running GPU jobs experience suboptimal performance after hours or days of execution, which translates into longer job completion times and wasted GPU cluster resources. To address this problem, we present Theseus, a novel CCL backend that provides schedule-level runtime adaptivity. It admits user-defined schedules and selection policies. As runtime conditions change, Theseus selects suitable schedules using cluster-wide runtime attributes beyond CCL-internal metrics. Moreover, it hot-swaps from the previous schedule consistently across GPUs with low overhead. Theseus acts as a drop-in replacement to facilitate integration. We evaluate Theseus extensively on various GPU workloads with intuitive policies. Compared with NCCL, Theseus achieves up to 1.61X speedup of communication time in stable environments and 2.46X in dynamic environments. It improves end-to-end job completion time by up to 1.84X while incurring comparable or lower overhead.
Rui Ding, Xiandong Lu, Jiajun Wang et al.· Proceedings of the ACM SIGCO...· 0 citations
With the rapid growth of artificial intelligence (AI) and high-performance computing (HPC), GPUs and other accelerators have become a shared computing substrate for a wide range of workloads. However, many shared accelerator clusters still rely on coarse device-level allocation, which often leads to low effective utilization, resource fragmentation, and long queueing delays. Although pooling technologies offer a promising direction, existing approaches remain limited in supporting fine-grained, low overhead sharing across heterogeneous accelerators and diverse co-located workloads. This paper presents gPooling, a hardware-agnostic accelerator pooling framework based on driver-level interception. gPooling creates elastic virtual devices on demand and extends fine-grained sharing across heterogeneous accelerators through a unified control path. We evaluate gPooling using benchmarks derived from real cluster traces and through deployment in a production GPU cluster. Results show that gPooling improves accelerator utilization, reduces user waiting time, and increases the overall efficiency of shared accelerator environments.
Kaicheng Guo, Jingyi Chen, Chen Chen et al.· IEEE Transactions on Paralle...· 0 citations
This work presents a data-driven framework that leverages historical job traces to estimate the impact of resource modifications on queue performance, and introduces the Weighted Wait-Time Score (WWS), a bounded metric that captures both typical and tail wait-time behavior.
Bipin Gaikwad, Shraddha Singh, M. Joshi et al.· Practice and Experience in A...· 0 citations
SA-Scheduler is presented, a structure-derived bottleneck-aware scheduling framework for multitasking MCM-GPUs that determines chip placement without hardware modification or runtime bottleneck profiling, and provides a principled and scalable foundation for multitasking on future MCM-GPUs.
Tiejian Zhang, Guangda Zhang, Lu Wang et al.· ACM Transactions on Design A...· 0 citations