A Comparative Study of Scheduling Techniques for Multimedia Applications on SIMD Pipelines

Parallel architectures are essential in order to take advantage of the parallelism inherent in streaming applications. One particular branch of these employ hardware SIMD pipelines. In this paper, we analyse several scheduling techniques, namely ad hoc overlapped execution, modulo scheduling and modulo scheduling with unrolling, all of which aim to efficiently utilize the special architecture design. Our investigation focuses on improving throughput while analysing other metrics that are important for streaming applications, such as register pressure, buffer sizes and code size. Through experiments conducted on several media benchmarks, we present and discuss trade-offs involved when selecting any one of these scheduling techniques.Comment: Presented at DATE Friday Workshop on Heterogeneous Architectures and Design Methods for Embedded Image Systems (HIS 2015) (arXiv:1502.07241

A Scala Embedded DSL for Combinatorial Optimization in Software Requirements Engineering

The goal of the presented work is to provide support for software requirements engineering domain experts in modeling combinatorial optimization problems that arise in requirements prioritization and release planning. A Domain-Specific Language (DSL), called reqT/CSP, is presented that integrates constraints modeling with requirements modeling. The DSL is embedded in the object-functional Scala programming language. The DSL is demonstrated using principal examples of priority ranking and release planning. Benefits, limitations and future work are discussed

Distributed constraint programming with agents

Many combinatorial optimization problems lend themselves to be modeled as distributed constraint optimization problems (DisCOP). Problems such as job shop scheduling have an intuitive matching between agents and machines. In distributed constraint problems, agents control variables and are connected via constraints. We have equipped these agents with a full constraint solver. This makes it possible to use global constraint and advanced search schemes. By empowering the agents with their own solver, we overcome the low performance that often haunts distributed constraint satisfaction problems (DisCSP). By using global constraints, we achieve far greater pruning than traditional DisCSP models. Hence, we dramatically reduce communication between agents. Our experiments show that both global constraints and advanced search schemes are necessary to optimize job shop schedules using DisCSP

Parallel consistency in constraint programming

Program parallelization becomes increasingly important when new multi-core architectures provide ways to improve performance. One of the greatest challenges of this development lies in programming parallel applications. Using declarative languages, such as constraint programming, can make the transition to parallelism easier by hiding the parallelization details in a framework. Automatic parallelization in constraint programming has previously focused on data parallelism. In this paper, we look at task parallelism, specifically the case of parallel consistency. We have developed two models of parallel consistency, one that shares intermediate results and one that does not. We evaluate which model is better in our experiments. Our results show that parallelizing consistency can provide the programmer with a robust scalability for regular problems with global constraints

Combining parallel search and parallel consistency in constraint programming

Program parallelization becomes increasingly important when new multi-core architectures provide ways to improve performance. One of the greatest challenges of this development lies in programming parallel applications. Declarative languages, such as constraint programming, can make the transition to parallelism easier by hiding the parallelization details in a framework. Automatic parallelization in constraint programming has mostly focused on parallel search. While search and consistency are intrinsically linked, the consistency part of the solving process is often more time-consuming. We have previously looked at parallel consistency and found it to be quite promising. In this paper we investigate how to combine parallel search with parallel consistency. We evaluate which problems are suitable and which are not. Our results show that parallelizing the entire solving process in constraint programming is a major challenge as parallel search and parallel consistency typically suit different types of problems

Area and Reconfiguration Time Minimization of the Communication Network in Regular 2D Reconfigurable Architectures

International audienceIn this paper, we introduce a constraint programming- based approach for the optimization of area and of reconfiguration time for communication networks for a class of regular 2D reconfigurable processor array architectures. For a given set of different algorithms the execution of which is supposed to be switched upon request at run-time, we provide static solutions for the optimal routing of data between processors. Here, we support also multi-casting data transfers for the first time. The routing found by our method minimizes the area or the reconfiguration time of the communication network, when switching between the execution of these algorithms. In fact, when switching, the communication network reconfiguration can be executed in just a few clock cycles. Moreover the communication network area can be minimized signifiHeidelbergcantly (62% in average)

Beyond von Neumann: weakly programmable processor arrays and their programming

The age of parallelism is here. For a sustainable software development for massively parallel architectures the von Neumann model need to be replaced by one with native support for parallelism. We suggest a data flow model for signal processing applications. This will make it possible to reuse software implementations for different targets and future platform generations. We also outline our development tool flow for compiling CAL, a data flow language, to parallel architectures. We also present our processor array, which can be configured to handle massively parallel computations. We demonstrate its power by implementing part of a software radio receiver

Exploring Software Product Management decision problems with constraint solving - opportunities for prioritization and release planning

Decision-making is central to Software Product Management (SPM) and includes deciding on requirements priorities and the content of coming releases. Several algorithms for prioritization and release planning have been proposed, where humans with or without machine support enact a series of steps to produce a decision outcome. Instead of applying some specific algorithm to find an acceptable solution to a decision problem, we propose to model SPM decision-making as a Constraint Satisfaction Problem (CSP), where relative and absolute priorities, interdependencies, and other constraints are expressed as relations among variables representing entities such as feature priorities, stakeholder preferences, and resource constraints. The solution space is then explored with the help of a constraint solver without humans needing to care about specific algorithms. This paper discusses advantages and limitations of CSP modeling in SPM and gives principal examples as a proof-of-concept of CSP modeling in requirements prioritization and release planning. A discussion of further research on constraint solving in SPM is also given

Scheduling of Conditional Process Graphs for the Synthesis of Embedded Systems

We present an approach to process scheduling based on an abstract graph representation which captures both dataflow and the flow of control. Target architectures consist of several processors, ASICs and shared busses. We have developed a heuristic which generates a schedule table so that the worst case delay is minimized. Several experiments demonstrate the efficiency of the approach. 1