Dynamic Dataflow. Seminar on embedded systems

Transcription

1 Dynamic Dataflow Seminar on embedded systems

2 Dataflow Dataflow programming, Dataflow architecture Dataflow Models of Computation Computation is divided into nodes that can be executed concurrently

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4 Dataflow Models of Computation Directed graph Data is split into tokens Tokens flow between the nodes Asynchronous execution of the nodes

5 Problems Tokens have to be buffered between the actors Unbounded execution is allowed 1. Unbounded buffer growth 2. Execution may deadlock when there are not enough tokens to continue

6 Synchronous Dataflow The number of tokens produced and consumed by an actor is fixed Guarantees bounded buffers and deadlock free execution Drawback: limited expressive power

7 Dynamic Dataflow Number of tokens produced and consumed by an actor in a single firing is not constrained Improved expression power over SDF Analysis more difficult compared to SDF

8 Dynamic Actors Variable number of tokens produced / consumed A control input

9 A conditional structure constructed with DDF actors Control tokens produced by actor B control the execution of either C or D Other constructs such as loops can be constructed as well

10 Dynamic Dataflow Scheduling SDF graphs can be scheduled statically at compile time DDF graphs can have data dependencies and may therefore require runtime scheduling Sometimes a hybrid approach is used In RVC-CAL the graphs are divided into maximal statically schedulable portions The runtime scheduler schedules these subgraphs

11 Cal Actor Language Used in MPEG RVC-CAL for standardising parts of the video encoding / decoding pipeline Commonly used as an example of a dynamic dataflow implementation Relatively mature toolchain and lots of examples available at

12 TensorFlow Machine Learning library by Google Uses Dynamic dataflow model of computation Graph iterations Conditional execution of actors Stateful actors Control dependencies Supports parallel processing on heterogeneous platforms Has both C++ and Python APIs

13 Conclusion The demand for intuitive and powerful ways to describe parallel computations is growing. One answer to the demand is the use of dynamic dataflow models of computation. DDF MoCs have been a subject of research for decades but have failed to garner widespread support among practical users. With the introduction of new DDF frameworks such as TensorFlow and Naiad, the DDF models have a chance of making it to the mainstream.

14 DDF Experiment

15 Experiment Pick up a problem that DDF has been used to solve Use Open Event Machine to implement solution

16 Open Event Machine Runtime system for Multicore platforms Dynamically load balanced applications with runto-completion principle Original implementation by Nokia Solutions and Networks

17 Terminology Event is the unit of communication in OpenEM. Events are typically used to carry data to process but can be data-less tokens as well. Corresponds to tokens in the DDF MoC Execution objects encapsulate the algorithm to execute when an event is received. Counterpart to the Actors in DDF Queues connect events (data) and execution objects (algorithms). Each queue is associated with one execution object and all queued events will be processed by this execution object. Scheduler moves allocated events to queues User calls the dispatcher in dispatch loop. Dispatcher calls the receive function of the Execution object of the connected queue

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19 Problem The common examples studied in the papers are Highly complex (HEVC decoder with over 20k slocs) Or too simple to make sense to implement with OpenEM

20 Solution Pick a task that DDF is used for Implement the relevant parts with OpenEM Approximate the rest

21 Experiment Outline Input: video stream Utilise TI vision library functions to extract simple features from frames The high level structure of the application is similar to licence plate recognition for example Detect Cars > Detect Licence Plates > OCR the licence number Approximate shape recognition by some simple thresholding I.e. frame contains >N edges Compute a heavy operation Frame contains <N edges Drop frame and continue

22 Introduction to Embedded Systems E. A. Lee and S. A. Seshia Scheduling Dynamic Dataflow Graphs PiSDF K. Desnos, M. Pelcat, J.-F. Nezan, S. S. Bhattacharyya, and S. Aridhi Handbook of Signal Processing Systems, Dynamic Dataflow Graphs S. S. Bhattacharyya, E. F. Deprettere, R. Leupers, and J. Takala CAL Actor Language J.Ekerand, J.Janneck Cal Language Report Google TensorFlow Library