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Implementing Delay Multiply and Sum Beamformer on a Hybrid CPU-GPU Platform for Medical Ultrasound Imaging Using Open MP and CUDA 被引量:2
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作者 Ke Song Paul Liu Dongquan Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第9期1133-1150,共18页
Anovel beamforming algorithmnamed Delay Multiply and Sum(DMAS),which excels at enhancing the resolution and contrast of ultrasonic image,has recently been proposed.However,there are nested loops in this algorithm,so t... Anovel beamforming algorithmnamed Delay Multiply and Sum(DMAS),which excels at enhancing the resolution and contrast of ultrasonic image,has recently been proposed.However,there are nested loops in this algorithm,so the calculation complexity is higher compared to the Delay and Sum(DAS)beamformer which is widely used in industry.Thus,we proposed a simple vector-based method to lower its complexity.The key point is to transform the nested loops into several vector operations,which can be efficiently implemented on many parallel platforms,such as Graphics Processing Units(GPUs),and multi-core Central Processing Units(CPUs).Consequently,we considered to implement this algorithm on such a platform.In order to maximize the use of computing power,we use the GPUs andmulti-core CPUs inmixture.The platform used in our test is a low cost Personal Computer(PC),where a GPU and a multi-core CPU are installed.The results show that the hybrid use of a CPU and a GPU can get a significant performance improvement in comparison with using a GPU or using amulti-core CPU alone.The performance of the hybrid system is increased by about 47%–63%compared to a single GPU.When 32 elements are used in receiving,the fame rate basically can reach 30 fps.In the best case,the frame rate can be increased to 40 fps. 展开更多
关键词 BEAMFORMING delay multiply and sum graphics processing unit multi-core central processing unit
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基于负载均衡的CPU-GPU异构计算平台任务调度策略 被引量:5
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作者 方娟 章佳兴 《北京工业大学学报》 CAS CSCD 北大核心 2020年第7期782-787,共6页
针对中央处理单元-图形处理单元(central processing unit-graphics processing unit,CPU-GPU)异构计算系统中,CPU和GPU负载不均导致系统性能降低的问题,提出了一种基于队列的混合调度策略.该策略通过探测获得CPU和GPU处理指定任务的计... 针对中央处理单元-图形处理单元(central processing unit-graphics processing unit,CPU-GPU)异构计算系统中,CPU和GPU负载不均导致系统性能降低的问题,提出了一种基于队列的混合调度策略.该策略通过探测获得CPU和GPU处理指定任务的计算能力,将计算任务按照探测比例分配给CPU和GPU;将并行任务存入双向队列,以降低调度带来的额外开销.结果表明,使用该策略的基准测试程序系统性能平均提升了28.07%.总体而言,该调度策略能够缩短CPU与GPU完成各自计算任务后的等待时间,有效平衡系统CPU与GPU之间的负载,提升系统性能. 展开更多
关键词 中央处理单元-图形处理单元(central processing unit-graphics processing unit CPU-GPU) 异构计算 高性能计算 任务调度 负载均衡 负载感知
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