Nowadays, most embedded devices need to support multiple applications running concurrently. In contrast to desktop computing, very often the set of applications is known at design time and the designer needs to assure that critical applications meet their constraints in every possible use-case. In order to do this, all possible use-cases, i.e. subset of applications running simultaneously, have to be verified thoroughly. An approach to reduce the verification effort, is to perform composability analysis which has been studied for sets of applications modeled as Synchronous Dataflow Graphs. In this paper we introduce a framework that supports a more general parallel programming model based on the Kahn Process Networks Model of Computation and integrates a complete MPSoC programming environment that includes: compilercentric analysis, performance estimation, simulation as well as mapping and scheduling of multiple applications. In our solution, composability analysis is performed on parallel traces obtained by instrumenting the application code. A case study performed on three typical embedded applications, JPEG, GSM and MPEG-2, proved the applicability of our approach.

Abstract-Microarchitecture research and development rely heavily on simulators. The ideal simulator should be simple and easy to develop, it should be precise, accurate and very fast. But the ideal simulator does not exist, and microarchitects use different sorts of simulators at different stages of the development of a processor, depending on which is most important, accuracy or simulation speed. Approximate microarchitecture models, which trade accuracy for simulation speed, are very useful for research and design space exploration, provided the loss of accuracy remains acceptable. Behavioral superscalar core modeling is a possible way to trade accuracy for simulation speed in situations where the focus of the study is not the core itself. In this approach, a superscalar core is viewed as a black box emitting requests to the uncore at certain times. A behavioral core model can be connected to a cycle-accurate uncore model. Behavioral core models are built from cycle-accurate simulations. Once the time to build the model is amortized, important simulation speedups can be obtained. We describe and study a new method for defining behavioral models for modern superscalar cores. The proposed Behavioral Application-Dependent Superscalar Core model, BADCO, predicts the execution time of a thread running on a superscalar core with an error less than 10 % in most cases. We show that BADCO is qualitatively accurate, being able to predict how performance changes when we change the uncore. The simulation speedups we obtained are typically between one and two orders of magnitude.

Abstract-Most high-performance processors today are able to execute multiple threads of execution simultaneously. Threads share processor resources, like the last-level cache, which may decrease throughput in a non obvious way, depending on threads' characteristics. Computer architects usually study multiprogrammed workloads by considering a set of benchmarks and some combinations of these benchmarks. Because detailed microarchitecture simulators are slow, we want a subset of combinations that is as small as possible, yet representative. However, there is no standard method for selecting such sample, and different authors have used different methods. It is not clear how the choice of a particular sample impacts the conclusions of a study. We propose and compare different sampling methods for defining multiprogrammed workloads for computer architecture studies. We evaluate their effectiveness with a case study, the comparison of several multicore last-level cache replacement policies. We show that random sampling, the simplest method, is a possible way to define a representative workload sample, provided the sample is large enough. We propose a method for estimating the required sample size based on fast approximate simulation. We propose a new method, workload stratification, which is very effective at reducing the sample size in situations where random sampling would require large samples.

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