Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Apart from the obvious determinants for cost-efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Although memory issues in consumer-class GPUs could pass unnoticed since these cards do not support ECC memory, unreliable GPUs can be sorted out with memory checking tools. For inexpensive consumer-class GPUs this improvement equally reflects in the performance-to-price ratio. Adding any type of GPU significantly boosts a node's simulation performance. Though hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance-to-price ratio, energy efficiency, and several other criteria. Here we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. Hardware features are well exploited with a combination of SIMD, multi-threading, and MPI-based SPMD/MPMD parallelism, while GPUs can be used as accelerators to compute interactions offloaded from the CPU. The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P). Only then will you receive your login details so that you can benefit from the advantages of the MTERM app.In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications-a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Tip: To be able to use the MTERM app, you must be a Cashback World Loyalty Merchant. You can offer your customers Shopping Benefits in the form of Cashback and Shopping Points at any time, even while you are out making a delivery or at your stall on the market – resulting in increased mobility and better service for you and your customers. Regardless of whether you use MTERM at your POS or on the move - you can access your MTERM app wherever you are!įind new loyal customers, any time, any place. Every Member who takes advantage of this service from you will automatically become your loyal customer. This gives you the chance to allow all Members of the Shopping Community to pick-up your business' Cashback Card from your store. What's more, you can enjoy a host of other benefits if you sign up as a Card Pick-Up Point. ✔ Logging existing Cashback World members as loyal customers ✔ Logging sales recorded on the Cashback Card The app comes with a range of handy functions for Cashback World Loyalty Merchants: With the MTERM app, you have access to a tool for an efficient use of the Cashback Solutions customer loyalty program. Description of MTERM App (from google play)
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