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电子工程代写|计算机系统结构代写Computer Systems Architecture代考|Discussion and Conclusion

The stick pulling experiment clearly indicates the tradeoff between creating chances to collaborate and ensuring that resources are not depleted. Each stick site can be seen as a resource that needs to be populated, because if they are underused the system performance suffers. However, the resource ‘space’ between the sites is limited. Hence, we find an optimum system size that balances the use of stick sites and space.

The parallel optimization experiment indicates the tradeoff between collaborating while not loosing too much diversity. If there is no collaboration, each robot independently tries to optimize the problem. If each robot is connected to every other robot, then the search is not parallelized anymore but all robots investigate the same problem instances in parallel. There is clearly an optimum between sharing some information (a medium information flow through the system) and sharing too much information.

Gunther’s interpretation of his Universal Scaling Law speaks of contention (i.e., overhead in sharing resources) and lack of coherence (e.g., as in cache hierarchies). While contention can be easily identified in the multi-robot setup (e.g., for linearly scaled commuting times in the stick pulling scenario, see Fig. 4(b)), a correspondence to ‘lack of coherency’ is difficult to be identified. Instead we see two contradicting uses of shared resources in the stick pulling scenario. One resource is supposed to be populated to increase profit (stick sites) but the other resource is already depleted and creates overheads (space). In the parallel optimization scenario, there is also no lack of coherence but instead a too intensive communication that then crucially reduces exploration in the system.

Superlinearity seems more frequent in multi-robot systems and swarm systems probably mainly due to physical effects. In tasks, such as collectively pulling a heavy object and passing a gap or a steep hill, one or a few robots basically achieve zero performance (they cannot pull the object at all due to friction, they can just not pass the gap or the hill) but once a certain threshold $N_{c}$ of system size $N>N_{c}$ is reached the performance increases rapidly. Superlinearity as seen in the stick pulling scenario, however, is more subtle and less easily connected directly to such a single cause. Obviously it is the interplay of not underusing one resource while not depleting another.

电子工程代写|计算机系统结构代写Computer Systems Architecture代考|Basic Fingerprinting

During the execution of an application, a flow of instructions is executed. This flow is not homogeneous in terms of type of instructions, source of the instructions, and execution time of instructions. Accordingly, measuring for example the number of executed floating point instructions per time unit will lead to a characteristic curve of an application or a part of the application. If the application is executed several times with the same input parameters the measured curves are very similar (if sample rates greater than $1 \mu$ are applied). For tracking the progress of a known application, its measured curve can be compared to the recorded reference curve.

In case an application executed on a multicore processor suffers from interferences with other applications on the shared memory hierarchy, its progress is slowed down. Slowing down the application will result in a stretched (in time) but shrunk (in the value range) curve. When comparing such a mutated measured curve with the original reference curve, the actual slowdown can not only be identified but also be quantified at any time during execution.

Many current MPSoC (e.g. based on ARM, PowerPC) include performance counters implemented in hardware which can be configured to increment every time a given event is raised. While the amount of events which can be configured is usually more than 100 , the amount of counters that can be incremented simultaneously is small (around 4 to 6) [16]. An example of such curves is shown in Fig. $2 .$

The Fingerprint model is obtained by the execution of the main application several (thousand) times without other applications running in parallel. The performance counter values of the selected events are recorded with the frequency defined by the safety net system ( $100 \mu$ s period in the prototype FPGA case). Afterwards, the recorded characteristics are clustered in order to reduce the amount of curves that are combined into a model.

电子工程代写|计算机系统结构代写Computer Systems Architecture代考|CS1533

电子工程代写|计算机系统结构代写Computer Systems Architecture代考|Discussion and Conclusion

拉杆实验清楚地表明了在创造合作机会和确保资源不枯竭之间的权衡。每个棒状站点都可以被视为需要填充的资源,因为如果它们未被充分利用,系统性能就会受到影响。但是,站点之间的资源“空间”是有限的。因此,我们找到了平衡棒位和空间使用的最佳系统尺寸。

并行优化实验表明了协作与不失去太多多样性之间的权衡。如果没有协作,每个机器人都会独立尝试优化问题。如果每个机器人都连接到其他机器人,则搜索不再并行化,而是所有机器人并行调查相同的问题实例。在共享一些信息(通过系统的中等信息流)和共享太多信息之间显然存在最佳选择。

Gunther 对他的 Universal Scaling Law 的解释谈到了争用(即共享资源的开销)和缺乏连贯性(例如,在缓存层次结构中)。虽然在多机器人设置中可以很容易地识别出争用(例如,对于拉杆场景中的线性缩放通勤时间,参见图 4(b)),但很难识别与“缺乏一致性”的对应关系。相反,我们在拉杆场景中看到了共享资源的两种相互矛盾的用途。一种资源应该被填充以增加利润(粘性站点),但另一种资源已经耗尽并产生开销(空间)。在并行优化场景中,也不缺乏一致性,而是过于密集的通信,这会严重减少系统中的探索。

多机器人系统和群系统中的超线性似乎更频繁,这可能主要是由于物理效应。在任务中,比如集体拉一个重物,通过一个缝隙或陡峭的山坡,一个或几个机器人基本实现零性能(它们完全不能因为摩擦力拉动物体,它们只是不能通过缝隙或山坡) ) 但一旦达到某个阈值ñC系统规模ñ>ñC达到性能迅速提高。然而,在拉杆场景中看到的超线性更加微妙,不太容易直接与这样一个单一的原因联系起来。显然,这是不充分利用一种资源同时又不耗尽另一种资源的相互作用。

电子工程代写|计算机系统结构代写Computer Systems Architecture代考|Basic Fingerprinting

在应用程序执行期间,执行指令流。该流程在指令类型、指令来源和指令执行时间方面不是同质的。因此,例如测量每单位时间执行的浮点指令的数量将导致应用程序或应用程序的一部分的特性曲线。如果应用程序使用相同的输入参数执行多次,则测量曲线非常相似(如果采样率大于1米被应用)。为了跟踪已知应用程序的进度,可以将其测量曲线与记录的参考曲线进行比较。

如果在多核处理器上执行的应用程序受到与共享内存层次结构上其他应用程序的干扰,则其进度会减慢。减慢应用程序将导致(及时)拉伸但收缩(在值范围内)曲线。当将这种突变的测量曲线与原始参考曲线进行比较时,不仅可以识别实际的减速,还可以在执行过程中随时量化。

许多当前的 MPSoC(例如基于 ARM、PowerPC)包括在硬件中实现的性能计数器,可以将其配置为在每次引发给定事件时递增。虽然可以配置的事件数量通常超过 100,但可以同时递增的计数器数量很少(大约 4 到 6)[16]。此类曲线的一个示例如图 1 所示。2.

指纹模型是通过主应用程序执行数(千)次获得的,而其他应用程序没有并行运行。所选事件的性能计数器值以安全网系统定义的频率记录(100米s 期间在原型 FPGA 案例中)。然后,对记录的特征进行聚类,以减少组合成模型的曲线数量。

电子工程代写|计算机系统结构代写Computer Systems Architecture代考

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