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201 records · Page 12

Landrush: Rethinking In-Situ Analysis for GPGPU Workflows

In-situ analysis on the output data of scientific simulations has been made necessary by ever-growing output data volumes and increasing costs of data movement as supercomputing is moving towards exascale. With hardware accelerators like GPUs becoming increasingly common in high end machines, new opportunities arise to co-locate scientific simulations and online analysis performed on the scientific data generated by the simulations. However, the asynchronous nature of GPGPU programming models and the limited context-switching capabilities on the GPU pose challenges to co-locating the scientific simulation and analysis on the same GPU. This paper dives deeper into these challenges to understand how best to co-locate analysis with scientific simulations on the GPUs in HPC clusters. Specifically, our 'Landrush' approach to GPU sharing proposes a solution that utilizes idle cycles on the GPU to provide an improved time-to-answer, that is, the total time to run the scientific simulation and analysis of the generated data. Landrush is demonstrated with experimental results obtained from leadership high-end applications on ORNL's Titan supercomputer, which show that (i) GPU-based scientific simulations have varying degrees of idle cycles to afford useful analysis task co-location, and (ii) the inability to context switch on the GPU at instruction granularity can be overcome by careful control of the analysis kernel launches and software-controlled early completion of analysis kernel executions. Results show that Landrush is superior in terms of time-to-answer compared to serially running simulations followed by analysis or by relying on the GPU driver and hardwired thread dispatcher to run analysis concurrently on a single GPU.

Computer Science↗

T-FSM: A Scalable Distributed Task-Based System for Frequent Subgraph Pattern Mining from a Big Graph

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel and distributed systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, poor scale-out capability, and possibly inaccurate results. In this article, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. The execution engine of T-FSM supports both intra-machine parallelism and inter-machine parallelism. For intra-machine parallelism, T-FSM adopts a novel task-based execution model to ensure high multithreading concurrency, bounded memory consumption, and effective load balancing. For inter-machine parallelism, T-FSM ensures good scale-out performance with a lightweight pattern rebalancing approach that reduces workload skewness of pattern evaluations among machines. To avoid recomputing the contexts for migrated patterns, we design a novel context cache table to support concurrent and asynchronous requesting and caching of remote context data, which can timely evict and garbage collect used pattern contexts that are no longer needed to keep memory consumption bounded. Extensive experiments show that T-FSM is orders of magnitude faster than existing state-of-the-art parallel systems (more than 10×, 51×, 131×, 55× speedup over ScaleMine, DistGraph, Pangolin and Peregrine, respectively) and distributed systems (more than 42× and 88× over ScaleMine and DistGraph, respectively) for frequent subgraph pattern mining, and it scales out satisfactorily to 512 CPU cores on the Polaris supercomputer at Argonne National Laboratory.

97 MATHEMATICS AND COMPUTING↗

Simulation of IST Turbomachinery Power-Neutral Tests with the ANL Plant Dynamics Code

The validation of the Plant Dynamics Code (PDC) developed at Argonne National Laboratory (ANL) for the steady-state and transient analysis of supercritical carbon dioxide (sCO2) systems has been continued with new test data from the Naval Nuclear Laboratory (operated by Bechtel Marine Propulsion Corporation) Integrated System Test (IST). Although data from three runs were provided to ANL, only two of the data sets were analyzed and described in this report. The common feature of these tests is the power-neutral operation of the turbine-compressor shaft, where no external power through the alternator was provided during the tests. Instead, the shaft speed was allowed to change dictated by the power balance between the turbine, the compressor, and the power losses in the shaft. The new test data turned out to be important for code validation for several reasons. First, the power-neutral operation of the shaft allows validation of the shaft dynamics equations in asynchronous mode, when the shaft is disconnected from the grid. Second, the shaft speed control with the compressor recirculation (CR) valve not only allows for testing the code control logic itself, but it also serves as a good test for validation of both the compressor surge control and the turbine bypass control actions, since the effect of the CR action on the loop conditions is similar for both of these controls. Third, the varying compressor-inlet temperature change test allows validation of the transient response of the precooler, a shell-and-tube heat exchanger. The first transient simulation of the compressor-inlet temperature variation Test 64661 showed a much slower calculated response of the precooler in the calculations than the test data. Further investigation revealed an error in calculating the heat exchanger tube mass for the PDC dynamic equations that resulted in a slower change in the tube wall temperature than measured. The transient calculations for both tests were done in two steps. The first step was done in the same fashion as the FY15 IST analysis, where the CR valve position and the turbine-compressor shaft speed were specified through the PDC input based on the test values. On the second step, the turbine-compressor shaft dynamics equations were invoked by specifying that the shaft is disconnected from the grid. In addition, the CR valve control was used to control the shaft speed, based on the turbine bypass control logic already implemented in the PDC. For the shaft power balance, the friction (windage) loss is calculated based on the shaft balance at the steady-state conditions and is assumed to be scaled to the third power of shaft speed in the transient. Both the steady-state and transient simulations of both tests showed good agreement with the test data. The only significant difference was the turbine performance, which was not predicted as well as it was in the previous IST simulation, resulting in the prediction of a somewhat different flow split between the two turbines. This flow split difference is believed to be the result of not addressing the recent turbine modifications in the model. In addition, the full simulation of the turbine-compressor speed variation Test 65261-P with shaft speed control showed greater a difference with the test data later in the transient than the other test. Further analysis of the results revealed that this difference is most likely due to scaling the shaft windage losses only with the shaft speed and ignoring its dependency on the fluid density in the shaft cavity. Based on the results of steady state and transient calculations of the Tests 64661 and 65216-P, several areas of future improvements for the PDC simulation of the IST are identified.

42 ENGINEERING↗