关于different time的几种方法

本文通过C语言演示了如何使用time_t和clock_t来测量程序运行时间,并比较了两者的效果。展示了利用time()、clock()等函数进行时间戳获取及时间差计算的方法。
#include "time.h"
#include "stdio.h"
main()



/*
time_t lt; 
lt=time(NULL);
/ *printf("%f\n",lt);* /
printf(ctime(lt)); 
printf(asctime(localtime(lt)));
printf(asctime(gmtime(lt))); 
*/




time_t start,end;
int i;
start=time(NULL);
for(i=0;i<30000;i++)

printf("\1\1\1\1\1\1\1\1\1\1\n");
}
end=time(NULL);
printf("\1: The start is %2.7f\n",start);
printf("\1: The end is %2.7f\n",end);
printf("\1: The different is %6.3f\n",difftime(end,start));
/*
clock_t start,end;
int i;
double var;
start=clock();
for(i=0;i<10000;i++)

printf("\1\1\1\1\1\1\1\1\1\1\n");
}
end=clock();
printf("\1: The different is %6.3f\n",(double)(end-start));
*/

}

在计算时间差方面,clock_t,和time_t效果基本一致,只是各自有自已的api函数,用起来的基本差不多。Linux下面还有一个要注意sleep和usleep的和在windows下面有差距。

### FFT IP Core Supported Modes and Configurations in FPGA Design In the context of Field Programmable Gate Array (FPGA) designs, Fast Fourier Transform (FFT) Intellectual Property (IP) cores are essential components used to perform frequency analysis efficiently. These cores support multiple operational modes that cater to different application requirements. #### Radix-2 Burst I/O Mode The radix-2 burst input/output mode is designed for applications requiring high throughput with minimal latency. In this configuration, data enters and exits the FFT block as bursts rather than streaming continuously[^1]. This approach allows efficient use of memory resources while maintaining performance levels suitable for real-time processing tasks such as digital signal processing or wireless communication systems. #### Streaming Mode Streaming mode enables continuous flow of complex samples into and out from the FFT engine without interruption between frames. It provides flexibility regarding how many points can be processed at once since it does not require fixed-size blocks like those seen in burst operations. Instead, users define parameters including point size dynamically during runtime through control signals connected directly to the core interface[^2]. #### Pipelined Architecture Support To further enhance speed capabilities within FPGAs, some advanced FFT implementations incorporate pipelining techniques which allow overlapping computation stages across clock cycles. By doing so, these architectures achieve higher operating frequencies compared to non-pipelined alternatives when implemented inside modern devices equipped with sufficient logic elements available for mapping purposes. ```python # Example Python code snippet demonstrating a simple way to configure an FFT IP core using PyVHDL. from pyvhdl import Entity, Port, Signal, StdLogicVector, ComponentInstantiation class FFTEngine(Entity): """Simple representation of configuring an FFT IP core.""" def __init__(self): super().__init__() self.clk = Port('in', std_logic=True) self.resetn = Port('in', std_logic=True) # Configuration settings for FFT IP core self.fft_size = Signal(StdLogicVector(width=8)) self.mode_select = Signal(std_logic_vector='0') # '0' -> Burst; '1'-> Stream # Instantiate actual hardware component here... fft_core_inst = ComponentInstantiation( name="fft_ip", entity=self, generics={ "N_POINT": self.fft_size, "MODE": self.mode_select } ) ``` --related questions-- 1. How do varying FFT sizes impact resource utilization on FPGAs? 2. What considerations should designers take into account when choosing between burst versus stream operation modes? 3. Can you provide examples where pipeline architecture significantly improves system performance over traditional methods? 4. Are there specific industries benefiting more prominently due to advancements made possible via optimized FFT algorithms?
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