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The SPLASH-2 suite of parallel applications has recently been released to facilitate the study of centralized and distributed sharedaddress-space multiprocessors.In this context, this paper has two goals. One is to quantitatively characterize the SPLASH-2 programs in terms of fundamental properties and architectural interactions that are important to understand them well. The properties we study include the computational load balance, communication to computation ratio and traffic needs, important working set sizes, and issues related to spatial locality, as well as how these properties scale with problem size and the number of processors. The other, related goal is methodological:to assist people who will use the programs in architectural evaluations to prune the space of application and machine parameters in an informed and meaningful way. For example, by characterizing the working sets of the applications, we describe which operating points in terms of cache size and problem size are representative of realistlc situations, which are not, and which re redundant. Using SPLASH-2 as an example, we hope to convey the importance of understanding the interplay of problem size, number of processors, and working sets in designing experiments and interpreting their results.

While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics for being a unified framework for both, batch and stream data processing. There is also a renewed interest in Near Data Processing (NDP) due to technological advancement in the last decade. However, it is not known if NDP architectures can improve the performance of big data processing frameworks such as Apache Spark. In this paper, we build the case of NDP architecture comprising programmable logic based hybrid 2D integrated processing-in-memory and instorage processing for Apache Spark, by extensive profiling of Apache Spark based workloads on Ivy Bridge Server.

Although shared memory multiprocessors are becoming increasingly popular in the commercial market place, the applications used to evaluate such systems in both academia and industry are still predominantly technical applications such as the Stanford SPLASHZ[I I benchmarks. The di#icirl~ in using commercial parallel shared memory applications such as transaction processing, decision support and web server applications has been in simulating the operating systems functions that are heavily used by these applications. In this paper we describe the design of an execution driven simulation tool called COMPASS (COMmercial PArallel Shared memory Simulaior). We have used COMPASS at IBM to study the behavior of decision support applications and are currently studying the behavior of transaction processing applications and web servers.

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