Energy consumption by distribution warehouses has become an essential component of green warehousing and research on reducing the carbon footprint of supply chains. Energy consumption in warehousing is a complex and multilayered problem, which is generally considered in the literature in relation to its detailed components, not as part of comparative studies. In this article, the authors consider six cross-sectional variants of warehouse technology, from manual to fully automatic, and analyze the energy consumption of a warehouse in various configurations. A methodology for estimating storage space and determining energy consumption is proposed. The energy balance of the warehouse variants includes energy for material handling equipment operation, energy consumption for building maintenance (heating, cooling, lighting, etc.), and energy generated by the photovoltaic system on the roof. Then, the operational costs of the variants are estimated and, on their basis, an automation index is determined. The index allows for a comparative analysis of energy consumption and the mechanization and automation of a warehouse. It is shown that a significant part of the energy is spent on maintaining a warehouse building, especially in the case of facilities with a low degree of automation.

The multidimensional positive definite advection transport algorithm (MPDATA) belongs to the group of nonoscillatory forward-in-time algorithms and performs a sequence of stencil computations. MPDATA is one of the major parts of the dynamic core of the EULAG geophysical model. In this work, we outline an approach to adaptation of the 3D MPDATA algorithm to the Intel MIC architecture. In order to utilize available computing resources, we propose the (3 + 1)D decomposition of MPDATA heterogeneous stencil computations. This approach is based on combination of the loop tiling and fusion techniques. It allows us to ease memory/communication bounds and better exploit the theoretical floating point efficiency of target computing platforms. An important method of improving the efficiency of the (3 + 1)D decomposition is partitioning of available cores/threads into work teams. It permits for reducing inter-cache communication overheads. This method also increases opportunities for the efficient distribution of MPDATA computation onto available resources of the Intel MIC architecture, as well as Intel CPUs. We discuss preliminary performance results obtained on two hybrid platforms, containing two CPUs and Intel Xeon Phi. The top-of-the-line Intel Xeon Phi 7120P gives the best performance results, and executes MPDATA almost 2 times faster than two Intel Xeon E5-2697v2 CPUs.

Running computational science applications on the emerging cloud infrastructures requires appropriate programming models and tools. In this paper we investigate the applicability of the component model to developing such applications. The component model we propose takes advantages of the features of the IaaS infrastructure and offers a high-level application composition API. We describe experiments on a scientific application from the bioinformatics domain, using a hybrid cloud infrastructure which consists of a private cloud running Eucalyptus and the Amazon EC2 public cloud. The measured performance of virtual machine startup time and virtualization overhead indicate promising prospects for exploiting such infrastructures along with the proposed component-based approach