CAJ | 학술논문

为克服交叉相关外推算法时间复杂度高、运算时间过长的缺点，提出一种基于GPU的快速并行化算法，应用于地闪落点的外推预测。首先分析串行的算法流程，然后对算法进行并行化分析设计，再针对AMD系列GPU硬件架构特点，运用OpenCL技术从主存与设备内存之间的数据传输、显存访问模式等方面对算法进一步优化。最后将地闪监测实况数据与本算法外推计算结果进行比对，分析不同精度下串行与并行算法的计算效率。实验结果表明，该算法充分利用GPU强大的并行计算能力，计算速度提高了近17倍。
위극복교차상관외추산법시간복잡도고、운산시간과장적결점，제출일충기우GPU적쾌속병행화산법，응용우지섬낙점적외추예측。수선분석천행적산법류정，연후대산법진행병행화분석설계，재침대AMD계렬GPU경건가구특점，운용OpenCL기술종주존여설비내존지간적수거전수、현존방문모식등방면대산법진일보우화。최후장지섬감측실황수거여본산법외추계산결과진행비대，분석불동정도하천행여병행산법적계산효솔。실험결과표명，해산법충분이용GPU강대적병행계산능력，계산속도제고료근17배。
To overcome the shortcomings of high time complexity and long computing time about cross -correlation extrapolation al-gorithm, an improved GPU-based fast parallel algorithm is presented , which is applied to extrapolate the Cloud-to-Ground flash development trend .First of all, we analyze the serial algorithm flow , and then design the parallel algorithm flow , optimize the al-gorithm by way of improving the data transfer between device memory and main memory , and optimize the memory access pat-terns.These optimization measures are based on OpenCL technology and aimed at the hardware architecture of AMD series GPU . Finally, we compare the Cloud-to-Ground flash monitoring data against the extrapolation results computed by this algorithm , and analyze the efficiency of the serial and the parallel algorithms under different precisions .The experimental result indicates that , the algorithm takes advantage of the powerful GPU parallel computing capability , and the calculation speed increases by nearly 17 times.