International Journal of Advanced Research in Computer Science and Software Engineering

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Volume 2, Issue 1, October 212 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.

Rahul J Shetty,Shraddha Joshi Abstract The following paper describes in detail the Embedded CPU benchmarking algorithm that we have developed.

1 Volume 2, Issue 1, October 212 ISSN: X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: Special Issue: Recent Trend in Computing Conference Held in SRM University, NCR Campus, India Speed Comparison for Embedded Processors Using Infinite Series Samuel Noah Ramrajkar, SheltonD souza, Rahul J Shetty,Shraddha Joshi Abstract The following paper describes in detail the Embedded CPU benchmarking algorithm that we have developed. The paper gives a brief description about the existing benchmarking methods. It then talks about our algorithm used for benchmarking and finally ends with the results that we found in a tabular and graphic format. Keywords: benchmarking, Synthetic Programs I. INTRODUCTION Processors are ubiquitous in today's world. They interpret and implement software instructions, perform calculations and comparisons, make logical decisions and interact with other devices. Almost any electronic device contains a processor, and their computing power ranges from a few thousand to millions of instruction per second. They come in many sizes and shapes, vary in speed and have different instruction sets, are designed for different use, and so their capabilities vary greatly. Even within a specific area, processors are different enough to make it difficult to compare them. Benchmarks provide a method of comparing the performance of processors and rank them among their peers. The term benchmark was created and used by surveyors. After surveying an area, surveyors chiselled marks in which they placed an angle-iron to bracket (bench) the rod to indicate the exact position from where they took their measurements for future reference. From there, the word benchmark found its way into computer science CPU benchmarking is a type of measurement for computer performance that is applied to the central processing unit, or CPU, of the computer system. These measurements provide a set of standards that allow users to compare the performance of different machines under the same circumstances. In CPU benchmarking, observers often assess the clock rate or other types of processor functions. The benchmark provides the core standard for the design of a CPU, or simply be recorded in quality or production research. II. TYPES OF BENCHMARKING Benchmarks can be roughly divided into 5 groups. Those groups are: 1. Addition Instruction 2. Instruction Mixes 3. Kernels 4. Synthetic Programs 5. Application Benchmarks 1) Addition Instruction Earlier, computers had very few instructions and the addition instruction was the most common instruction so performance was measured by counting how many additions a computer could perform per unit time. 2) Instruction Mixes Measurements based on a single instruction had the disadvantage that they did not take into account that other instructions had different complexities. Hence, instruction mixes was used. The number of times each instruction appears in the mix should ideally reflect the frequency with that the instruction appears in an average program. However, emerging processors continued to increase the number of instructions in the instruction set making it difficult to include all the instructions in the instruction mix. Also, with the introduction of advanced features like pipelining and address translations, execution time of instruction started to depend instructions executed before and after the instruction. 3) Kernels Kernel benchmarks are based on analysis and the knowledge that in most cases 1 percent ofthe code uses 8 percent of the CPU resources. Performance analysts have extracted these codefragments and have used them as benchmarks.the fundamental problems with kernelbenchmarks are that they are usually small, fit in cache, are prone to attack by compilers, andmeasure only CPU performance. The disadvantage of Kernels is that they do not reflect the behaviour of a typical application of issuing system calls and interacting with I/O devices. 212, IJARCSSE All Rights Reserved Page 37

2 Samuel et al., International Journal of Advanced Research in Computer Science and Software Engineering 2 (1), October- 212, pp ) Synthetic Programs Synthetic programs are developed by exercising a mix of computations, system calls and I/O requests. The main advantage of synthetic programs is that they can be quickly developed and do not rely on real data. To assure portability, these programs are written in a high level language. Types of synthetic benchmark are: a. Whetstone The Whetstone benchmark primarily measures the floating point arithmetic performance. It contains ten modules that perform a variety of numerical computations (e.g., arrays, trigonometric functions). The benchmark is small and sensitive to the ordering of library modules and size of caches. The performance metrics are single- and double precision Whetstones per second b. Dhrystone This synthetic benchmark spends significant time on string functions. It was designed to measure the integer performance of small machines with simple architectures. The performance metric is Dhrystones per second. These were the first general purpose industry standard benchmarks. The main drawback of synthetic programs is that they are too small to make statistically relevant memory or disk references. Similar issues arise with page faults, disk cache hits/misses, and the CPU-I/O overlap. 5) Application Benchmarks By far, the most accurate way to get measurements reflecting application program performance is to use applications as benchmarks. a set of 'typical' applications is used to compute an overall performance score. These benchmarks are generally identical to the original applications with the exception of small modifications made to automate the evaluation process. Application benchmarks are also not perfect since they evaluate the whole computer instead of just the processor. Embedded processors are yet another kind of processor. They are typically designed for a specific task. Embedded processors usually run on a lower voltage and frequency, have fewer instructions, and have narrower instruction words. Recently however, embedded processors have become more and more powerful. Nevertheless, embedded processors are different enough to justify their own benchmarks. III. OUR ALGORITHM The benchmarking algorithm is based on the principle of counting the number of terms of a power series e^x. The value of x for the current benchmarking was fixed at an arbitrary value of two. The series is given by e x = 1 + x 1! + x2 2! + x3 3! +. This clearly shows that the computations per term increases exponentially and this can be a real identifier of the speed of the CPU under test Following are the main components of the algorithm. A. Main() This part of the code is the entry function for the code after start-up. It begins by the initialization of the timer (usually of timer ) for the micro-controller so that it can interrupt the processor at duration of 1 sec. This is followed by initialization of the UART module to a predefined baud rate of 192 for displaying the count of terms on the PC screen next section initializes the timer interrupt and only then allows the timer to run. The heart of the algorithm follows this in the while (1) loop. The calculation for the terms starts and goes on till 1 second. The variable terms is the global variable where the value of terms calculated is available any time. Note that the compiler is made aware that it is a shared data variable using volatile modifier.. B. TIMER_ISR() This is the interrupt handler used for timer interrupt that converts the counted terms in to decimal number. It then displays this count on the PC screen. It uses the %1 1 algorithm for this. The ISR then locks up the processor in an infinite loop as the test for the speed is complete. C. send_uart_data This function is a used to send the data to the UART port. The basic idea behind the benchmarking is the fact that more the number of terms calculated more is the speed of the CPU under test. Also it is worth to guess that the calculation of the terms becomes increasingly complex with the 212, IJARCSSE All Rights Reserved Page 38

3 Samuel et al., International Journal of Advanced Research in Computer Science and Software Engineering 2 (1), October- 212, pp increase in the index. This algorithm we believe lies in the segment of Synthetic Programs. The synthesis can easily prove the processing power of an embedded CPU in situations where heavy duty calculations are needed. Such applications could be DSP applications like the digital filter or signal reconstruction algorithms. This may also prove the worth of a CPU for running applications needing the super loop type of execution because the calculation of the terms itself is in a super loop. IV. THE BENCHMARKING CODE void send_uart_data(unsigned char); //function to send data volatile unsigned short int terms; //actual terms calculated void TIMER_Isr() irq unsigned char data[1];//to store the digits of count unsigned short int temp; unsigned char i,j; //disable timer interrupt i=; temp=terms; while(temp!=) data[i]=temp%1; //%1 1 algorithm temp=temp/1; i++; i--; for(j=;j<3;j++) send_uart_data(data[i] x3); i--; while(1); //soft stop the program void main() doubleans; unsignedintvar,index; index=; ans=; var=2; terms=; //set timer to interrupt on 1 sec //enable timer interrupt //enable the UART here //enable timer only now to start counting while(1) unsignedinttemp,i; doublepow; long double fact; temp=index; pow=1; fact=1; for(i=temp;i>;i--) pow=pow*var; for(i=temp;i>;i--) fact=fact*i; ans=ans+(double)(pow/fact); index++; 212, IJARCSSE All Rights Reserved Page 39

4 Number of terms Number of terms Samuel et al., International Journal of Advanced Research in Computer Science and Software Engineering 2 (1), October- 212, pp terms++; V. OBSERVATIONS The following are the tabular and graphical results obtained when the benchmarking algorithm was run on the selected processor. The tables indicate if the results were obtained from simulations, practical hardware or both. A. Cortex M3 (lpc1768) TABLE I OBSERVATIONS FOR CORTEX M3 of operation (in MHz) Terms/sec (simulated) Terms/sec (hardware) y = -.47x x Fig.1 Graph for Cortex M3- Number of terms vs. B. ARM7 (lpc2148) TABLE III OBSERVATIONS FOR ARM7 of Terms/sec operation (in MHz) (simulated) Terms/sec (hardware) y = -.41x x , IJARCSSE All Rights Reserved Page 4

5 Number of Terms Samuel et al., International Journal of Advanced Research in Computer Science and Software Engineering 2 (1), October- 212, pp Fig. 2 Graph for ARM7 Number of terms vs. C. AVR ATMEGA 328 TABLE IIIII OBSERVATIONS FOR AVR of operation (in Terms/sec MHz) (simulated) y = -.516x x D. PIC18 (PIC18F452) Fig. 3 Graph for AVR ATMEGA Number of terms vs. TABLE IVV OBSERVATIONS FOR PIC18 of operation (in Terms/sec MHz) (simulated) y = -.128x x E. 851 (NXP P89V51RD2) Fig. 4. Graph for PIC18 Number of terms vs. TABLE V OBSERVATIONS FOR 851 of operation (in Terms/sec (simulated) MHz) , IJARCSSE All Rights Reserved Page 41

6 Number of terms Samuel et al., International Journal of Advanced Research in Computer Science and Software Engineering 2 (1), October- 212, pp y = -.8x x VI. CONCLUSIONS The benchmarking code was successfully executed in simulation and in hardware. The benchmarking results are selfexplanatory an provide a quick selection guide for an embedded designer to select his processor when core speed is a selection criterion. The algorithm is a better choice as it evaluates the true computing power of the CPU. As the number of terms increases it is worthy to note that the processor is really very powerful as the computing steps increases exponentially with each term. VII.FUTURE SCOPE The benchmarking algorithm is to be extended to RTOS applications with a fixed pair of processor and RTOS can be benched marked with the score of (terms/sec)/process. The work on this will soon commence. REFERENCES [1] Benjamin C Lee. An Architectural Assessment of SPECCPU Benchmark Relevance Internet: ftp://ftp.deas.harvard.edu/techreports/tr-2-6.pdf [2] Christopher Cullian, ChristopherWyant, Timothy Frattesi. Computing Performance Benchmarks among CPU, GPU, and FPGA Internet: [3] Internet: / [4] SHARC Processor Benchmarks Internet:/ [5] Computing Performance Benchmarks among CPU, GPU, and FPGA. Internet: [6] Exponential Function. Internet: 212, IJARCSSE All Rights Reserved Page 42

Q.1 Explain Computer s Basic Elements

Q.1 Explain Computer s Basic Elements Ans. At a top level, a computer consists of processor, memory, and I/O components, with one or more modules of each type. These components are interconnected in some