![]() Taking that into account, nodes with a well-balanced ratio of CPU and consumer-class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime. 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. A video card or graphics card, such as the AMD FireStream 9370, is another type of expansion card. It is geared for use in servers and high performance GPU computing engines or accelerators. 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 AMD Firestream 9370’s passive cooler occupies the width of two expansion slots and keeps the card and its environment relatively cool during operation. 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). Display ConnectorsĪPIs supported, including particular versions of those APIs.In this paper we investigate the use of distributed GPU-based architectures to accelerate pipelined wavefront applications a ubiquitous class of parallel algorithm used for the solution of a number of scientific and engineering applications. OEM manufacturers may change the number and type of output ports, while for notebook cards availability of certain video outputs ports depends on the laptop model rather than on the card itself. As a rule, data in this section is precise only for desktop reference ones (so-called Founders Edition for NVIDIA chips). Types and number of video connectors present on the reviewed GPUs. Note that GPUs integrated into processors have no dedicated VRAM and use a shared part of system RAM. Parameters of memory installed: its type, size, bus, clock and resulting bandwidth. FireStream 9370 - PCIVEN1002&DEV688C Mobility Radeon HD. ![]() #AMD FIRESTREAM 9370 BENCHMARK DRIVERS#For desktop video cards it's interface and bus (motherboard compatibility), additional power connectors (power supply compatibility). Download latest mobility drivers for AMD/ATI Radeon HD 6310 and Microsoft Windows 7 32bit. Useful when choosing a future computer configuration or upgrading an existing one. Information on compatibility with other computer components. Pipelines / CUDA coresĬompatibility, dimensions and requirements Note that power consumption of some graphics cards can well exceed their nominal TDP, especially when overclocked. These parameters indirectly speak of performance, but for precise assessment you have to consider their benchmark and gaming test results. General performance parameters such as number of shaders, GPU core base clock and boost clock speeds, manufacturing process, texturing and calculation speed. ![]()
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