Fully Homomorphic Encryption (FHE) is increasingly adopted in Online Analytical Processing (OLAP) systems to protect against data breaches. However, existing FHE-based OLAP systems must sequentially execute computationally intensive homomorphic comparisons for query processing on FHE ciphertexts, leading to significantly lower performance compared to traditional OLAP systems. We present Opera, the first high-performance FHE-based OLAP system on the GPU. Observing the redundancy in re-executing homomorphic comparisons from scratch, we design Homcache to create a GPU-accelerated parallel query execution workflow: Opera selectively caches comparison results and allows subsequent comparisons to reuse them, thereby reducing the computational cost per comparison and facilitating concurrent execution of multiple comparisons on the GPU. Nevertheless, due to the large size of FHE ciphertexts, Homcache can grow substantially, and naively applying traditional plaintext-oriented cache management strategies like LRU results in suboptimal performance. To ensure stable high performance, we develop a density-driven algorithm tailored for managing ciphertexts in Homcache. Compared to notable baselines running on CPUs, Opera reduces query latency by up to 9612x with 1.2GB cache storage without compromising security. OPERA's source code, complete benchmark suite, and raw results are available at github.com/hku-systems/Opera.

Supernet training, a prevalent and important paradigm in Neural Architecture Search, embeds the whole DNN architecture search space into one monolithic supernet, iteratively activates a subset of the supernet (i.e., a subnet) for fitting each batch of data, and searches a high-quality subnet which meets specific requirements. Although training subnets in parallel on multiple GPUs is desirable for acceleration, there inherently exists a race hazard that concurrent subnets may access the same DNN layers. Existing systems support neither efficiently parallelizing subnets’ training executions, nor resolving the race hazard deterministically, leading to unreproducible training procedures and potentiallly non-trivial accuracy loss.

Flow scheduling is crucial in data centers, as it directly influences user experience of applications. According to different assumptions and design goals, there are four typical flow scheduling problems/solutions: SRPT, LAS, Fair Queueing, and Deadline-Aware scheduling. When implementing these solutions in commodity switches with limited number of queues, they need to set static parameters by measuring traffic in advance, while optimal parameters vary across time and space. This paper proposes a generic framework, namely QCluster, to adapt all scheduling problems for limited number of queues. The key idea of QCluster is to cluster packets with similar weights/properties into the same queue. QCluster is implemented in Tofino switches, and can cluster packets at a speed of 3.2 Tbps. To the best of our knowledge, QCluster is the fastest clustering algorithm. Experimental results in testbed with programmable switches and ns-2 show that QCluster reduces the average flow completion time (FCT) for short flows up to 56.6%, and reduces the overall average FCT up to 21.7% over state-of-the-art. All the source code in ns-2 is available in Github [45].