Search for: All records

The current trend of performance growth in HPC systems is accompanied by a massive increase in energy consumption. In this article, we introduce GreenMD, an energy-efficient framework for heterogeneous systems for LU factorization utilizing multi-GPUs. LU factorization is a crucial kernel from the MAGMA library, which is highly optimized. Our aim is to apply DVFS to this application by leveraging slacks intelligently on both CPUs and multiple GPUs. To predict the slack times, accurate performance models are developed separately for both CPUs and GPUs based on the algorithmic knowledge and manufacturer’s specifications. Since DVFS does not reduce static energy consumption, we also develop undervolting techniques for both CPUs and GPUs. Reducing voltage below threshold values may give rise to errors; hence, we extract the minimum safe voltages ( V safeMin ) for the CPUs and GPUs utilizing a low overhead profiling phase and apply them before execution. It is shown that GreenMD improves the CPU, GPU, and total energy about 59%, 21%, and 31%, respectively, while delivering similar performance to the state-of-the-art linear algebra MAGMA library. 

The k-core of a graph is the largest induced sub-graph with minimum degree k. The problem of k-core decomposition finds the k-cores of a graph for all valid values of k, and it has many applications such as network analysis, computational biology and graph visualization. Currently, there are two types of parallel algorithms for k-core decomposition: (1) degree-based vertex peeling, and (2) iterative h-index refinement. There is, however, few studies on accelerating k-core decomposition using GPU. In this paper, we propose a highly optimized peeling algorithm on a GPU, and compare it with possible implementations on top of think-like-a-vertex graph-parallel GPU systems as well as existing serial and parallel k-core decomposition algorithms on CPUs. Extensive experiments show that our GPU algorithm is the overall winner in both time and space. Our source code is released at https://github.com/akhlaqueak/KCoreGPU. 

A significant challenge on an exascale computer is the speed at which we compute results exceeds by many orders of magnitude the speed at which we save these results. Therefore the Exascale Computing Project (ECP) ALPINE project focuses on providing exascale-ready visualization solutions including in situ processing. In situ visualization and analysis runs as the simulation is run, on simulations results are they are generated avoiding the need to save entire simulations to storage for later analysis. The ALPINE project made post hoc visualization tools, ParaView and VisIt, exascale ready and developed in situ algorithms and infrastructures. The suite of ALPINE algorithms developed under ECP includes novel approaches to enable automated data analysis and visualization to focus on the most important aspects of the simulation. Many of the algorithms also provide data reduction benefits to meet the I/O challenges at exascale. ALPINE developed a new lightweight in situ infrastructure, Ascent.