GPU multiplexing is a widely-adopted strategy in GPU clusters for improving overall throughput and lowering the total cost of ownership. To mitigate inter-task interference in compute power and memory bandwidth on multiplexed GPUs, existing techniques divide a GPU into instances with limited predefined rigid configurations. Low utilization arises from the mismatch between heterogeneous burstiness and immutable resource configurations: 1) bursty inference traffic forces the scheduler to launch underfilled batches that cannot saturate the instance; 2) bursty kernel resource utilization leads to bubbles in compute power and memory bandwidth.

Modern web applications use databases to store their data. When processing user requests, these applications retrieve and store data in the database server, which incurs network round trips. These round trips significantly increase the application's latency. Previous approaches have attempted to reduce these round trips by prefetching query results or batching database accesses. However, neither method can efficiently reduce the latency when some queries depend on previous queries' results. In real-world applications, nearly 50% of the queries depend on the result of other queries. This paper presents WeBridge, the first system capable of synthesizing stored procedures for large-scale real-world web applications. First, WeBridge employs concolic execution technique to analyze the applications and generate stored procedures for hot program paths. Then, it seamlessly integrates the stored procedures into the application by extending the database access library. Finally, it improves the efficiency of the stored procedures with speculative execution. Evaluation using real-world web applications and workloads show that WeBridge achieves up to 79.8% median latency reduction and up to 2× peak throughput.