Profiles of Upcoming HPC Applications and Their Impact on Reservation Strategies

Abstract

With the expected convergence between HPC, BigData and AI, new applications with different profiles are coming to HPC infrastructures. We aim at better understanding the features and needs of these applications in order to be able to run them efficiently on HPC platforms. The approach followed is bottom-up: we study thoroughly an emerging application, Spatially Localized Atlas Network Tiles (SLANT, originating from the neuroscience community) to understand its behavior. Based on these observations, we derive a generic, yet simple, application model (namely, a linear sequence of stochastic jobs). We expect this model to be representative for a large set of upcoming applications that require the computational power of HPC clusters without fitting the typical behavior of large-scale traditional applications. In a second step, we show how one can manipulate this generic model in a scheduling framework. Specifically we consider the problem of making reservations (both time and memory) for an execution on an HPC platform. We derive solutions using the model of the first step of this work. We experimentally show the robustness of the model, even with very few data or with another application, to generate the model, and provide performance gains with regards to standard and more recent approaches used in the neuroscience community.

Optimizing performance and energy across problem sizes through a search space exploration and machine learning

“…One may expect that larger sizes lead to larger memory footprints and longer execution times, while offering greater opportunities for parallel computation. It is known that this is not always the case, as sometimes the qualitative aspects of an application's data have a deeper effect than their quantitative aspects [18]. Nonetheless, it would be convenient to discover good configurations for small, cheap to process sizes that remain relevant for larger, more time-consuming problems.…”

Optimal Checkpointing Strategies for Iterative Applications

“…Neuroscience application For the first application scenario, we extracted data from a representative neuroscience application, Spatially Localized Atlas Network Tiles (SLANT) [25]. The tasks composing an iteration of this application are described in Table 1, extracted from [17]. GCR application For the second application scenario, we consider a class of Krylov Subspace method GCR [14] solving the m-dimensional sparse linear system Ax = b.…”