计算机科学
现场可编程门阵列
图形处理单元的通用计算
浮点型
超级计算机
并行计算
绘图
图形处理单元
协处理器
功率消耗
点(几何)
计算机体系结构
计算科学
嵌入式系统
功率(物理)
算法
计算机图形学(图像)
量子力学
数学
物理
几何学
作者
Mário P. Véstias,Horácio C. Neto
标识
DOI:10.1109/fpl.2014.6927483
摘要
Floating-point computing with more than one TFLOP of peak performance is already a reality in recent Field-Programmable Gate Arrays (FPGA). General-Purpose Graphics Processing Units (GPGPU) and recent many-core CPUs have also taken advantage of the recent technological innovations in integrated circuit (IC) design and had also dramatically improved their peak performances. In this paper, we compare the trends of these computing architectures for high-performance computing and survey these platforms in the execution of algorithms belonging to different scientific application domains. Trends in peak performance, power consumption and sustained performances, for particular applications, show that FPGAs are increasing the gap to GPUs and many-core CPUs moving them away from high-performance computing with intensive floating-point calculations. FPGAs become competitive for custom floating-point or fixed-point representations, for smaller input sizes of certain algorithms, for combinational logic problems and parallel map-reduce problems.
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