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ISPA: Exploiting Intra-SM Parallelism in GPUs via Fine-Grained Resource Management., , , and . IEEE Trans. Computers, 72 (5): 1473-1487 (May 2023)E2bird: Enhanced Elastic Batch for Improving Responsiveness and Throughput of Deep Learning Services., , , , , and . IEEE Trans. Parallel Distributed Syst., 32 (6): 1307-1321 (2021)Enable simultaneous DNN services based on deterministic operator overlap and precise latency prediction., , , , , , , , , and 1 other author(s). SC, page 15. ACM, (2021)Microless: Cost-Efficient Hybrid Deployment of Microservices on IaaS VMs and Serverless., , , , , and . ICPADS, page 2303-2310. IEEE, (2023)Maximizing the Utilization of GPUs Used by Cloud Gaming through Adaptive Co-location with Combo., , , , , , , and . SoCC, page 265-280. ACM, (2023)Improving Cluster Utilization Through Adaptive Resource Management for Deep Neural Network and CPU Jobs Colocation., , , , , and . IEEE Trans. Computers, 72 (12): 3458-3472 (December 2023)Tacker: Tensor-CUDA Core Kernel Fusion for Improving the GPU Utilization while Ensuring QoS., , , , , , , and . HPCA, page 800-813. IEEE, (2022)PAME: precision-aware multi-exit DNN serving for reducing latencies of batched inferences., , , , , , , and . ICS, page 37:1-37:12. ACM, (2022)Optimizing Dynamic Neural Networks with Brainstorm., , , , , , , , , and 4 other author(s). OSDI, page 797-815. USENIX Association, (2023)A Codesign of Scheduling and Parallelization for Large Model Training in Heterogeneous Clusters., , , , , , , , and . CoRR, (2024)