PiCo: A Domain-Specific Language for Data Analytics Pipelines

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models—for which only informal (and often confusing) semantics is generally provided—all share a common under- lying model, namely, the Dataflow model. Using this model as a starting point, it is possible to categorize and analyze almost all aspects about Big Data analytics tools from a high level perspective. This analysis can be considered as a first step toward a formal model to be exploited in the design of a (new) framework for Big Data analytics. By putting clear separations between all levels of abstraction (i.e., from the runtime to the user API), it is easier for a programmer or software designer to avoid mixing low level with high level aspects, as we are often used to see in state-of-the-art Big Data analytics frameworks. From the user-level perspective, we think that a clearer and simple semantics is preferable, together with a strong separation of concerns. For this reason, we use the Dataflow model as a starting point to build a programming environment with a simplified programming model implemented as a Domain-Specific Language, that is on top of a stack of layers that build a prototypical framework for Big Data analytics. The contribution of this thesis is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm, Google Dataflow), thus making it easier to understand high-level data-processing applications written in such frameworks. As result of this analysis, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level. Second, we propose a programming environment based on such layered model in the form of a Domain-Specific Language (DSL) for processing data collections, called PiCo (Pipeline Composition). The main entity of this programming model is the Pipeline, basically a DAG-composition of processing elements. This model is intended to give the user an unique interface for both stream and batch processing, hiding completely data management and focusing only on operations, which are represented by Pipeline stages. Our DSL will be built on top of the FastFlow library, exploiting both shared and distributed parallelism, and implemented in C++11/14 with the aim of porting C++ into the Big Data world

Profile driven dataflow optimisation of mean shift visual tracking

Profile guided optimisation is a common technique used by compilers and runtime systems to shorten execution runtimes and to optimise locality aware scheduling and memory access on heterogeneous hardware platforms. Some profiling tools trace the execution of low level code, whilst others are designed for abstract models of computation to provide rich domain-specific context in profiling reports. We have implemented mean shift, a computer vision tracking algorithm, in the RVC-CAL dataflow language and use both dynamic runtime and static dataflow profiling mechanisms to identify and eliminate bottlenecks in our naive initial version. We use these profiling reports to tune the CPU scheduler reducing runtime by 88%, and to optimise our dataflow implementation that reduces runtime by a further 43% - an overall runtime reduction of 93%. We also assess the portability of our mean shift optimisations by trading off CPU runtime against resource utilisation on FPGAs. Applying all dataflow optimisations reduces FPGA design space significantly, requiring fewer slice LUTs and less block memory

DKPN: A Composite Dataflow/Kahn Process Networks Execution Model

International audienceTo address the high level of dynamism and variability in modern streaming applications (e.g. video decoding) as well as the difficulties in programming heterogeneous MPSoCs, we propose a novel execution model based upon both dataflow and Kahn process networks. This paper presents the semantics and properties of this hierarchical and parametric model, called DKPN. Parameters are classified and it is shown that hints can be derived to improve the execution. A scheduler framework and policies to back the model are also exposed. Experiments illustrate the benefits of our approach

Area-energy aware dataflow optimisation of visual tracking systems

This paper presents an orderly dataflow-optimisation approach suitable for area-energy aware computer vision applications on FPGAs. Vision systems are increasingly being deployed in power constrained scenarios, where the dataflow model of computation has become popular for describing complex algorithms. Dataflow model allows processing datapaths comprised of several independent and well defined computations. However, compilers are often unsuccessful in identifying domain-specific optimisation opportunities resulting in wasted resources and power consumption. We present a methodology for the optimisation of dataflow networks, according to patterns often found in computer vision systems, focusing on identifying optimisations which are not discovered automatically by an optimising compiler. Code transformation using profiling and refactoring provides opportunities to optimise the design, targeting FPGA implementations and focusing on area and power abatement. Our refactoring methodology, applying transformations to a complex algorithm for visual tracking resulted in significant reduction in power consumption and resource usage