BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures

We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution is an execution plan optimization paradigm, namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair of producer-consumer operators into consideration. We propose a branch and bound based approach with three heuristics to resolve the resulting nontrivial optimization problem. The experimental evaluations demonstrate that BriskStream yields much higher throughput and better scalability than existing DSPSs on multi-core architectures when processing different types of workloads.Comment: To appear in SIGMOD'1

HARL: Hierarchical Adaptive Reinforcement Learning Based Auto Scheduler for Neural Networks

To efficiently perform inference with neural networks, the underlying tensor programs require sufficient tuning efforts before being deployed into production environments. Usually, enormous tensor program candidates need to be sufficiently explored to find the one with the best performance. This is necessary to make the neural network products meet the high demand of real-world applications such as natural language processing, auto-driving, etc. Auto-schedulers are being developed to avoid the need for human intervention. However, due to the gigantic search space and lack of intelligent search guidance, current auto-schedulers require hours to days of tuning time to find the best-performing tensor program for the entire neural network. In this paper, we propose HARL, a reinforcement learning (RL) based auto-scheduler specifically designed for efficient tensor program exploration. HARL uses a hierarchical RL architecture in which learning-based decisions are made at all different levels of search granularity. It also automatically adjusts exploration configurations in real-time for faster performance convergence. As a result, HARL improves the tensor operator performance by 22% and the search speed by 4.3x compared to the state-of-the-art auto-scheduler. Inference performance and search speed are also significantly improved on end-to-end neural networks

An Efficient E2E Verifiable E-voting System without Setup Assumptions

End-to-end (E2E) verifiability is critical if e-voting systems are to be adopted for use in real-world elections. A new E2E e-voting system doesn't require additional setup assumptions and uses conventional cryptographic building blocks

On the Solutions in the Global Attractor of the Incompressible Navier-Stokes Equations

We study the global attractor for the solutions of the incompressible Navier-Stokes equations (NSE) equipped with appropriate boundary conditions. A challenge in the cases when zero is not in the global attractor is to find sharp lower bound on the energy. A related challenging problem is to show that zero is in the attractor if and only if the external force is zero. We show that if zero were in the global attractor, then all its elements, as well as the external force, must be smooth functions. By exploring a particular family of function classes, we show that the set of nonzero external forces for which zero could be in the global attractor is meagre (of the first Baire category in a Fréchet topology). The weak global attractor of three dimensional Navier-Stokes equations is a complex geometric object. An interesting challenging question is to measure its complexity. Invoking the fact that topology on the weak global attractor can be metrizable, we use a physically reasonable metric function to obtain explicit estimate for the Kolmogorov e-entropy of the weak global attractor in terms of the physical parameter associated with the fluid flow. We also study the existence of the nonstationary solutions in the global attractor of the space periodic two dimensional NSE which have constant energy and enstropy per unit mass for all time. A subclass of such solutions whose geometric structures have a supplementary stability property is defined and explored. We prove that the wave vectors of the active mode of this subclass must satisfy a finite Galerkin system. The nonexistence of solutions in this subclass is proved for the particular case when the external force has a special property