Architecture of Computing Systems (Lecture Notes in Computer ...

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214 M.Y. Qadri, D. Matichard, and K.D. McDonald Maier In contrast to a synthetic benchmark, an application benchmark such as JetBench, is a realistic representation of the actual workload; however there are some deviations one has to apply to allow portability of the application on various platforms, which are discussed as follows. Generally, all real applications require a significant amount of I/O operations, which if were implemented in the benchmark would have restricted its portability [16]. Therefore the I/O performance of a platform can not be evaluated through the proposed benchmark. Secondly, as the application has to get executed in a target time period, excessive computations could have caused the benchmark to perform poorly on majority of low end systems. To avoid this problem the JetBench application covers a limited number of typical thermodynamic calculations used in jet engines. As a consequence of the restricted workload of the computations, it may seem small enough to high-end multicore systems that their actual performance may not be reported well, as in contrast to a low end multicore platform. A more detailed analysis of the benchmark on a number of cores is given in the following section. The JetBench application not only provides the user with an overview of the realtime performance of the system, but could also be used to discover optimum number of threads to achieve desired performance. The JetBench benchmark is mainly comprised of ALU centric operations such as integer/double multiplication, addition, and division for the computation of exponents, square roots, and calculations such as value of pi and degree-to-radian conversion. All these operations are based on real thermodynamic equations and operations required for a jet engine control unit. The benchmark structure is composed of 88.6% of the parallel portion as reported by thread analysis tools, and is described in the pseudo code given in Fig. 3 and the threading diagram in Fig. 4. JetBench Pseudo Code Inputs: Engine Type Data File Defining Speed, Altitude, Throttle, Deadline Initialization: Set Default Parameters Select Engine Type Open data file Parallel Section: Calculate Pi Read an input data point Calculate: Environment variables Thermodynamic parameters Engine geometry Engine performance Print Results If not EOF goto Parallel Section Print Results End Fig. 3. Pseudo code of the application
4 Results JetBench: An Open Source Real-Time Multiprocessor Benchmark 215 Fig. 4. JetBench Thread Structure To analyze the scalability of the benchmark, un-optimized executions of the application were carried out on shared memory multicore platform based on sixteen x86 CPUs running at 20MHz. The platform was simulated on Simics full system simulator [17], running Linux kernel 2.6.15 including symmetric multiprocessing support. The input dataset comprised of 30 data points and calculation deadlines were uniformly set as 9 sec. As the platform is running at a low clock frequency i.e. 20 MHz, a single thread per core was executed. The benchmark output timing per input data point is shown in Fig. 5. The graph shows normalized timing values against the set deadline time, i.e. 9 sec in this instance, which enables one to compare execution rate instead of execution time. It is worth noting that the execution rate is inconsistent for all the cores, also the rate decreases with the increase in number of cores. The reason behind is that the application is not prioritized statistically by the user but has been prioritized by the kernel itself. Secondly for any application increasing the number of threads beyond a certain level actually decreases performance since thread handling overhead will surpass the per thread execution time. This phenomenon is more observable, when running multiple threads per core where context switches depreciate the performance after a certain level of parallelism. It can be observed from Fig. 6 and 7 that the overall execution time for the application is around 230 sec for a 4 core machine; however the 8 core machine offers a minimum number of missed deadlines, i.e. 2. This is due to the fact that although for 8 cores platform, threading overhead is higher than for the four core machine, which also effects the computation time per thread. But for 4 cores or less the CPU workload has exceeded the available resource and therefore resulted in missing more deadlines than the later. The output from the benchmark execution thus allows the user to Fig. 5. Application execution rate for different number of cores
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Table of Contents XIII JetBench:AnO
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How to Enhance a Superscalar Proces
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130 M. Kayaalp et al. INSTRUCTION 1
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