Connected and autonomous vehicles (CAVs) have recently attracted a significant amount of attention both from researchers and industry. Numerous studies targeting algorithms, software frameworks, and applications on the CAVs scenario have emerged. Meanwhile, several pioneer efforts have focused on the edge computing system and architecture design for the CAVs scenario and provided various heterogeneous platform prototypes for CAVs. However, a standard and comprehensive application benchmark for CAVs is missing, hindering the study of these emerging computing systems. To address this challenging problem, we present CAVBench, the first benchmark suite for the edge computing system in the CAVs scenario. CAVBench is comprised of six typical applications covering four dominate CAVs scenarios and takes four datasets as standard input. CAVBench provides quantitative evaluation results via application and system perspective output metrics. We perform a series of experiments and acquire three systemic characteristics of the applications in CAVBench. First, the operation intensity of the applications is polarized, which explains why heterogeneous hardware is important for a CAVs computing system. Second, all applications in CAVBench consume high memory bandwidth, so the system should be equipped with high bandwidth memory or leverage good memory bandwidth management to avoid the performance degradation caused by memory bandwidth competition. Third, some applications have worse data instruction locality based on the cache miss observation, so the computing system targeting these applications should optimize the cache architecture. Last, we use the CAVBench to evaluate a typical edge computing platform and present the quantitative and qualitative analysis of the benchmarking results.
There are many classic benchmark suites in the traditional computing field, such as BigDataBench @cite_8 for big data computing, Parsec @cite_9 for parallel computing and HPCC @cite_34 for high-performance computing etc. However, for the computing scenario in CAVs, the benchmark research work is still at the beginning stage and can be divided into two categories according to their contents: datasets and workloads.
@cite_9 @cite_34 @cite_8
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© 2014 IEEE. Big data benchmark suites must include a diversity of data and workloads to be useful in fairly evaluating big data systems and architectures. However, using truly comprehensive benchmarks poses great challenges for the architecture community. First, we need to thoroughly understand the behaviors of a variety of workloads. Second, our usual simulation-based research methods become prohibitively expensive for big data. As big data is an emerging field, more and more software stacks are being proposed to facilitate the development of big data applications, which aggravates these challenges. In this paper, we first use Principle Component Analysis (PCA) to identify the most important characteristics from 45 metrics to characterize big data workloads from BigDataBench, a comprehensive big data benchmark suite. Second, we apply a clustering technique to the principle components obtained from the PCA to investigate the similarity among big data workloads, and we verify the importance of including different software stacks for big data benchmarking. Third, we select seven representative big data workloads by removing redundant ones and release the BigDataBench simulation version, which is publicly available from http: prof.ict.ac.cn BigDataBench simulatorversion . Recently, big data has been evolved into a buzzword from academia to industry all over the world. Benchmarks are important tools for evaluating an IT system. However, benchmarking big data systems is much more challenging than ever before. First, big data systems are still in their infant stage and consequently they are not well understood. Second, big data systems are more complicated compared to previous systems such as a single node computing platform. While some researchers started to design benchmarks for big data systems, they do not consider the redundancy between their benchmarks. Moreover, they use artificial input data sets rather than real world data for their benchmarks. It is therefore unclear whether these benchmarks can be used to precisely evaluate the performance of big data systems. In this paper, we first analyze the redundancy among benchmarks from ICTBench, HiBench and typical workloads from real world applications: spatio-temporal data analysis for Shenzhen transportation system. Subsequently, we present an initial idea of a big data benchmark suite for spatio-temporal data. There are three findings in this work: (1) redundancy exists in these pioneering benchmark suites and some of them can be removed safely. (2) The workload behavior of trajectory data analysis applications is dramatically affected by their input data sets. (3) The benchmarks created for academic research cannot represent the cases of real world applications. Benchmarks set standards for innovation in computer architecture research and industry product development. Consequently, it is of paramount importance that the benchmarks used in computer architecture research and development are representative of real-world applications. However, composing such representative workloads poses practical challenges to application analysis teams and benchmark developers - (1) Benchmarks that are standardized are open-source whereas applications of interest are typically proprietary, (2) Benchmarks are rigid, measure single-point performance, and only represent a sample of the application behavior space, (3) Benchmark suites take several years to develop, but applications evolve at a faster rate, and (4) Benchmarks geared towards temperature and power characterization are difficult to develop and standardize. The objective of this dissertation is to develop an adaptive benchmark generation strategy to construct synthetic benchmarks to address these benchmarking challenges. We propose an approach for automatically distilling key hardware-independent performance attributes of a proprietary workload and capture them into a miniature synthetic benchmark clone. The advantage of the benchmark clone is that it hides the functional meaning of the code, but exhibits similar performance and power characteristics as the target application across a wide range of microarchitecture configurations. Moreover, the dynamic instruction count of the synthetic benchmark clone is substantially shorter than the proprietary application, greatly reducing overall simulation time—for the SPEC CPU 2000 suite, the simulation time reduction is over five orders of magnitude compared to the entire benchmark execution. We develop an adaptive benchmark generation strategy that trades off accuracy to provide the flexibility to easily alter program characteristics. The parameterization of workload metrics makes it possible to succinctly describe an application's behavior using a limited number of fundamental program characteristics. This provides the ability to alter workload characteristics and construct scalable benchmarks that allows researchers to explore a wider range of the application behavior space, conduct program behavior studies, and model emerging workloads. The parameterized workload model is the foundation for automatically constructing power and temperature oriented synthetic workloads. We show that machine learning algorithms can be effectively used to search the application behavior space to automatically construct benchmarks for evaluating the power and temperature characteristics of a computer architecture design. The need for a scientific approach to construct synthetic benchmarks, to complement application benchmarks, has long been recognized by the computer architecture research community, and this dissertation work is a significant step towards achieving that goal. This paper presents and characterizes the Princeton Application Repository for Shared-Memory Computers (PARSEC), a benchmark suite for studies of Chip-Multiprocessors (CMPs). Previous available benchmarks for multiprocessors have focused on high-performance computing applications and used a limited number of synchronization methods. PARSEC includes emerging applications in recognition, mining and synthesis (RMS) as well as systems applications which mimic large-scale multithreaded commercial programs. Our characterization shows that the benchmark suite covers a wide spectrum of working sets, locality, data sharing, synchronization and off-chip traffic. The benchmark suite has been made available to the public.