Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the widespread use of hardware redundancies. However, these redundancies can inadvertently lead to hidden degradation, known as “gray failure”, for AI workloads, significantly affecting end-to-end performance and concealing performance issues, which complicates root cause analysis for failures and regressions. We introduce SuperBench, a proactive validation system for AI infrastructure that mitigates hidden degradation caused by hardware redundancies and enhances overall reliability. SuperBench features a comprehensive benchmark suite, capable of evaluating individual hardware components and representing most real AI workloads. It comprises a Validator that learns benchmark criteria to pinpoint defective components clearly. Additionally, SuperBench incorporates a Selector to balance validation time and issue-related penalties, enabling optimal timing for validation execution with a tailored subset of benchmarks. Through testbed evaluation and simulation, we demonstrate that SuperBench can increase the mean time between incidents by up to 22.61×. SuperBench has been successfully deployed in Azure production, validating hundreds of thousands of GPUs every year.

Data redistribution aims to reshuffle data to optimize some objective for an algorithm. The objective can be multi-dimensional, such as improving computational load balance or decreasing communication volume or cost, with the ultimate goal of increasing the efficiency and therefore reducing the time-to-solution for the algorithm. The classic redistribution problem focuses on optimally scheduling communications when reshuffling data between two regular, usually block-cyclic, data distributions. Besides distribution, data size is also a performance-critical parameter because it affects the reshuffling algorithm in terms of cache, communication efficiency, and potential parallelism. In addition, task-based runtime systems have gained popularity recently as a potential candidate to address the programming complexity on the way to exascale. In this scenario, it becomes paramount to develop a flexible redistribution algorithm for task-based runtime systems, which could support all types of regular and irregular data distributions and take data size into account. In this article, we detail a flexible redistribution algorithm and implement an efficient approach in a task-based runtime system,PaRSEC. Performance results show great capability compared to the theoreticalboundandScaLAPACK, and applications highlight an increased efficiency with little overhead in terms of data distribution, data size, and data format.

The micromobility is shaping first- and last-mile travels in urban areas. Recently, shared dockless electric scooters (e-scooters) have emerged as a daily alternative to driving for short-distance commuters in large cities due to the affordability, easy accessibility via an app, and zero emissions. Meanwhile, e-scooters come with challenges in city management, such as traffic rules, public safety, parking regulations, and liability issues. In this paper, we collected and investigated 5.8 million scooter-tagged tweets and 144,197 images, generated by 2.7 million users from October 2018 to March 2020, to take a closer look at shared e-scooters via crowdsourcing data analytics. We profiled e-scooter usages from spatial-temporal perspectives, explored different business roles (i.e., riders, gig workers, and ridesharing companies), examined operation patterns (e.g., injury types, and parking behaviors), and conducted sentiment analysis. To our best knowledge, this paper is the first large-scale systematic study on shared e-scooters using big social data.

As the scale of high-performance computing (HPC) systems continues to grow, increasing levels of parallelism must be implored to achieve optimal performance. Recently, the processors support wide vector extensions, vectorization becomes much more important to exploit the potential peak performance of target architecture. Novel processor architectures, such as the Armv8-A architecture, introduce Scalable Vector Extension (SVE) - an optional separate architectural extension with a new set of A64 instruction encodings, which enables even greater parallelisms. In this paper, we analyze the usage and performance of the SVE instructions in Arm SVE vector Instruction Set Architecture (ISA); and utilize those instructions to improve the memcpy and various local reduction operations. Furthermore, we propose new strategies to improve the performance of MPI operations including datatype packing/unpacking and MPI reduction. With these optimizations, we not only provide a higher-parallelism for a single node, but also achieve a more efficient communication scheme of message exchanging. The resulting efforts have been implemented in the context of OPEN MPI, providing efficient and scalable capabilities of SVE usage and extending the possible implementations of SVE to a more extensive range of programming and execution paradigms. The evaluation of the resulting software stack under different scenarios with both simulator and Fujitsu's A64FX processor demonstrates that the solution is at the same time generic and efficient.

Data redistribution aims to reshuffle data to optimize some objective for an algorithm. The objective can be multi-dimensional, such as improving computational load balance or decreasing communication volume or cost, with the ultimate goal to increase the efficiency and therefore decrease the time-to-solution for the algorithm. The classical redistribution problem focuses on optimally scheduling communications when reshuffling data between two regular, usually block-cyclic, data distributions. Recently, task-based runtime systems have gained popularity as a potential candidate to address the programming complexity on the way to exascale. In addition to an increase in portability against complex hardware and software systems, task-based runtime systems have the potential to be able to more easily cope with less-regular data distribution, providing a more balanced computational load during the lifetime of the execution. In this scenario, it becomes paramount to develop a general redistribution algorithm for task-based runtime systems, which could support all types of regular and irregular data distributions. In this paper, we detail a flexible redistribution algorithm, capable of dealing with redistribution problems without constraints of data distribution and data size and implement it in a task-based runtime system, PaRSEC. Performance results show great capability compared to ScaLAPACK, and applications highlight an increased efficiency with little overhead in terms of data distribution and data size.

High-performance computing (HPC) systems keep growing in scale and heterogeneity to satisfy the increasing computational need, and this brings new challenges to the design of MPI libraries, especially with regard to collective operations. To address these challenges, we present “HAN,” a new hierarchical autotuned collective communication framework in Open MPI, which selects suitable homogeneous collective communication modules as submodules for each hardware level, uses collective operations from the submodules as tasks, and organizes these tasks to perform efficient hierarchical collective operations. With a task-based design, HAN can easily swap out submodules, while keeping tasks intact, to adapt to new hardware. This makes HAN suitable for the current platform and provides a strong and flexible support for future HPC systems. To provide a fast and accurate autotuning mechanism, we present a novel cost model based on benchmarking the tasks instead of a whole collective operation. This method drastically reduces tuning time, as the cost of tasks can be reused across different message sizes, and is more accurate than existing cost models. Our cost analysis suggests the autotuning component can find the optimal configuration in most cases. The evaluation of the HAN framework suggests our design significantly improves the default Open MPI and achieves decent speedups against state-of-the-art MPI implementations on tested applications.

As the scale of high-performance computing (HPC) systems continues to grow, researchers are devoted themselves to explore increasing levels of parallelism to achieve optimal performance. The modern CPU’s design, including its features of hierarchical memory and SIMD/vectorization capability, governs algorithms’ efficiency. The recent introduction of wide vector instruction set extensions (AVX and SVE) motivated vectorization to become of critical importance to increase efficiency and close the gap to peak performance.

As the scale of high-performance computing (HPC) systems continues to grow, mean-time-to-failure (MTTF) of these HPC systems is negatively impacted and tends to decrease. In order to efficiently run long computing jobs on these systems, handling system failures becomes a prime challenge. We present here the design and implementation of an efficient runtime-level failure detection and propagation strategy targeting large-scale, dynamic systems that is able to detect both node and process failures. Multiple overlapping topologies are used to optimize the detection and propagation, minimizing the incurred overheads and guaranteeing the scalability of the entire framework. The resulting framework has been implemented in the context of a system-level runtime for parallel environments, PMIx Reference RunTime Environment (PRRTE), providing efficient and scalable capabilities of fault management to a large range of programming and execution paradigms. The experimental evaluation of the resulting software stack on different machines demonstrate that the solution is at the same time generic and efficient.

In this paper, a P2P pollution method named integrated pollution is proposed. It integrates the characteristic of fake-block-attack and index pollution to solve the limitation of each. The mathematic model of integrated pollution is built to discuss the factors which influence the effect of integrated pollution. The simulation of this model shows that the effect of integrated pollution depends on the amount of attackers, the amount of invalid peer information and the maximum connection of downloading peers. The influence of these 3 factors to the effect of integrated pollution is analyzed.