BACKGROUND: Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines. RESULTS: This paper presents a workflow system that makes progress on scaling machine learning ensembles, specifically in this first release, ensembles of deep neural networks that address problems in cancer research across the atomistic, molecular and population scales. The initial release of the application framework that we call CANDLE/Supervisor addresses the problem of hyper-parameter exploration of deep neural networks. CONCLUSIONS: Initial results demonstrating CANDLE on DOE systems at ORNL, ANL and NERSC (Titan, Theta and Cori, respectively) demonstrate both scaling and multi-platform execution.

In this paper, we present an application-level I/O caching, prefetching, asynchronous system to hide access latency experienced by HPC applications. Our solution of user controllable caching and prefetching system maintains a file-IO cache in the user space of the application, analyzes the I/O access patterns, prefetches requests, and performs write-back of dirty data to storage asynchronously. So each time the application needs the data it does not have to pay the full I/O latency penalty in going to the storage and getting the required data. We have implemented this caching and asynchronous access system on the Blue Gene (BG/L and BG/P) systems. We present experimental results with NAS BT, MADbench, and WRF benchmarks. The results on BG/P system demonstrate that our method hides access latency, enhances application I/O access time by as much as 100%, and improves WRF execution time over 10%.

In this paper, we present an application level aggressive I/O caching and prefetching system to hide I/O access latency experienced by out-of-core applications. Without the application level prefetching and caching capability, users of I/O intensive applications need to rewrite them with asynchronous I/O calls or restructure their code with MPI-IO calls to efficiently use the large scale system resources. Our proposed solution of user controllable aggressive caching and prefetching system maintains a file-IO cache in the user space of the application, analyzes the I/O access patterns, prefetches requests, and performs write-back of dirty data to storage asynchronously. So each time the application needs the data it does not have to pay the full I/O latency penalty in going to the storage and getting the required data. We have implemented this aggressive caching and asynchronous prefetching on the Blue Gene/P (BGP) system. The preliminary experiment evaluates the caching performance using the WRF benchmark. The results on BGP system demonstrate that our method improves application I/O throughput.