Using multiple streams can improve the overall system performance by mitigating the data transfer overhead on heterogeneous systems. Currently, very few cases have been streamed to demonstrate the streaming performance impact and a systematic investigation of streaming necessity and how-to over a large number of test cases remains a gap. In this paper, we use a total of 56 benchmarks to build a statistical view of the data transfer overhead, and give an in-depth analysis of the impacting factors. Among the heterogeneous codes, we identify two types of non-streamable codes and three types of streamable codes, for which a streaming approach has been proposed. Our experimental results on the CPU-MIC platform show that, with multiple streams, we can improve the application performance by up 90 . Our work can serve as a generic flow of using multiple streams on heterogeneous platforms.
have developed a framework for predicting the performance of applications executing on accelerators @cite_14 . Using automatically extracted application signatures and a machine profile based on benchmarks, they aim to predict the application running time before the application is ported. Evaluating offloading necessity is a former step of applying multiple streams. In this work, we evaluate the necessity of using multiple streams with a statistical approach.
@cite_14
2054506507 2109608899 2156678748 2136340918
Automated tools for understanding application behavior and its changes during the application lifecycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise-wide application can effect a large population of customers, lead to delayed projects, and ultimately can result in company financial loss. Significantly shortened time between new software releases further exacerbates the problem of thoroughly evaluating the performance of an updated application. Our thesis is that online performance modeling should be a part of routine application monitoring. Early, informative warnings on significant changes in application performance should help service providers to timely identify and prevent performance problems and their negative impact on the service. We propose a novel framework for automated anomaly detection and application change analysis. It is based on integration of two complementary techniques: (i) a regression-based transaction model that reflects a resource consumption model of the application, and (ii) an application performance signature that provides a compact model of runtime behavior of the application. The proposed integrated framework provides a simple and powerful solution for anomaly detection and analysis of essential performance changes in application behavior. An additional benefit of the proposed approach is its simplicity: It is not intrusive and is based on monitoring data that is typically available in enterprise production environments. The introduced solution further enables the automation of capacity planning and resource provisioning tasks of multitier applications in rapidly evolving IT environments. Hybrid-core systems speedup applications by offloading certain compute operations that can run faster on hardware accelerators. However, such systems require significant programming and porting effort to gain a performance benefit from the accelerators. Therefore, prior to porting it is prudent to investigate the predicted performance benefit of accelerators for a given workload. To address this problem we present a performance-modeling framework that predicts the application performance rapidly and accurately for hybrid-core systems. We present predictions for two full-scale HPC applications-HYCOM and Milc. Our results for two accelerators (GPU and FPGA) show that gather scatter and stream operations can speedup by as much as a factor of 15 and overall compute time of Milc and HYCOM improve by 3.4 and 20 , respectively. We also show that in order to benefit from the accelerators, 70 of the latency of data transfer time between the CPU and the accelerators needs to be overcome. Commercial Off-The-Shelf (COTS) processors are now commonly used in real-time embedded systems. The characteristics of these processors fulfill system requirements in terms of time-to-market, low cost, and high performance-per-watt ratio. However, multithreaded (MT) processors are still not widely used in real-time systems because the timing analysis is too complex. In MT processors, simultaneously-running tasks share and compete for processor resources, so the timing analysis has to estimate the possible impact that the inter-task interferences have on the execution time of the applications. In this paper, we propose a method that quantifies the slowdown that simultaneously-running tasks may experience due to collision in shared processor resources. To that end, we designed benchmarks that stress specific processor resources and we used them to (1) estimate the upper limit of a slowdown that simultaneously-running tasks may experience because of collision in different shared processor resources, and (2) quantify the sensitivity of time-critical applications to collision in these resources. We used the presented method to determine if a given MT processor is a good candidate for systems with timing requirements. We also present a case study in which the method is used to analyze three multithreaded architectures exhibiting different configurations of resource sharing. Finally, we show that measuring the slowdown that real applications experience when simultaneously-running with resource-stressing benchmarks is an important step in measurement-based timing analysis. This information is a base for incremental verification of MT COTS architectures. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users.