Modern mobile applications heavily rely on the notification system to acquire daily active users and enhance user engagement.Being able to proactively reach users, the system has to decide when to send notifications to users.Although many researchers have studied optimizing the timing of sending notifications, they only utilized users' contextual features, without modeling users' behavior patterns.Additionally, these efforts only focus on individual notifications, and there is a lack of studies on optimizing the holistic timing of multiple notifications within a period.To bridge these gaps, we propose the Temporal Interaction Model (TIM), which models users' behavior patterns by estimating CTR in every time slot over a day in our short video application Kuaishou.TIM leverages long-term user historical interaction sequence features such as notification receipts, clicks, watch time and effective views, and employs a temporal attention unit (TAU) to extract user behavior patterns.Moreover, we provide an elegant strategy of holistic notifications send time control to improve user engagement while minimizing disruption.We evaluate the effectiveness of TIM through offline experiments and online A/B tests.The results indicate that TIM is a reliable tool for forecasting user behavior, leading to a remarkable enhancement in user engagement without causing undue disturbance.

Based on the characteristics of the wireless sensor networks, a middleware layer for quality of service support was proposed. For kinds of user's QoS requirements, tasks of network were scheduled by collecting and updating local information continuously, and the reliability of network was enhanced by redundancy. Results of simulation show that the middleware mechanism can not only meet the user QoS requirements, but also reduce the energy consumption of network.

Heterogeneous architecture is becoming an important way to build a massive parallel computer system, i.e. the CPU-GPU heterogeneous systems ranked in Top500 list. However, it is a challenge to efficiently utilize massive parallelism of both applications and architectures on such heterogeneous systems. In this paper we present a practice on how to exploit and orchestrate parallelism at algorithm level to take advantage of underlying parallelism at architecture level. A potential Petaflops application -- cryo-EM 3D reconstruction is selected as an example. We exploit all possible parallelism in cryo-EM 3D reconstruction, and leverage a self-adaptive dynamic scheduling algorithm to create a proper parallelism mapping between the application and architecture. The parallelized programs are evaluated on a subsystem of Dawning Nebulae supercomputer, whose node is composed of two Intel six-core Xeon CPUs and one Nvidia Fermi GPU. The experiment confirms that hierarchical parallelism is an efficient pattern of parallel programming to utilize capabilities of both CPU and GPU in a heterogeneous system. The CUDA kernels run more than 3 times faster than the OpenMP parallelized ones using 12 cores (threads). Based on the GPU-only version, the hybrid CPU-GPU program further improves the whole application's performance by 30% on the average.

In this paper we present a thorough experience on tuning double-precision matrix-matrix multiplication (DGEM-M) on the Fermi GPU architecture. We choose an optimal algorithm with blocking in both shared memory and registers to satisfy the constraints of the Fermi memory hierarchy. Our optimization strategy is further guided by a performance modeling based on micro-architecture benchmarks. Our optimizations include software pipelining, use of vector memory operations, and instruction scheduling. Our best CUDA algorithm achieves comparable performance with the latest CUBLAS library. We further improve upon this with an implementation in the native machine language, leading to 20% increase in performance. That is, the achieved peak performance (efficiency) is improved from 302Gflop/s (58%) to 362Gflop/s (70%).