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Loops are the largest source of parallelism in many scientific applications. Parallelization of irregular loop applications is a challenging problem to achieve scalable performance on cluster and cloud systems. In distributed systems, load balance, communication and synchronization overhead must be taken ...
The growing complexity of high-performance computing (HPC) systems has led to the development of parallel programming models, such as OpenMP and OpenACC, to make it easier to utilize modern HPC architectures. These models provide a higher-level interface for specifying parallelism patterns and ...
parallelism.The emergence of cloud computing has drastically changed the scheduling requirements in recent years as performance constraints have become increasingly tight. Applications need to serve millions of queries per second while keeping each query's latency at microsecond scale, even in the worst...
First, our implementation is the first MapReduce based BLAST implementation that also uses database fragmentation -- a technique that is fundamental to exploit parallelism under low loads. Second, we use a locality enhancement technique that minimizes file transfers and improves performance.;Results ...
heterogeneous computing, and optics constitutes a critical step for both computing and optics.;The massive data parallelism, application dependent-location and function, as well as network latency, and bandwidth limitations facing networks today complement well with the strength of optical communications-ba...
, are also proposed.Thirdly, we investigate a novel DNN inference throughput maximization problem in an MEC network with the aim to maximize the number of delay-aware DNN service requests admitted, by accelerating each DNN inference through jointly exploring DNN partitioning and inference parallelism....
The block device abstraction fails to expose SSD parallelism and pass application requirements. To this end, we propose a software/hardware co-design to enforce performance isolation by bridging the semantic gap. Our design can significantly improve QoS (Quality of Service) by reducing throughput ...
We introduce new strategies for pipeline parallelism, with different tradeoffs between training throughput, memory footprint, and weight update semantics; these outperform existing methods in certain settings. Pipeline parallelism can also be used in conjunction with other forms of parallelism, helping ...
Many-task computing (MTC) applications assemble existing sequential (or parallel) programs, using POSIX files for intermediate data. The parallelism of such applications often comes from data parallelism. MTC applications can be grouped into stages, and dependencies between tasks in different stages can...