Tools and Benchmarks for Real-Time Systems

Schedulability and response time analysis • MECHAniSer - A Timing Analysis and Synthesis Tool for Multi-Rate Effect Chains with Job-Level Dependencies

2016-07-11T09:41:53+01:00

Tool Description
MECHAniSer is a tool to compute end-to-end delays in cause-effect chains. In such chains, each task is independently triggered, possibly at different rates. The communication between task is based on register communication.

The current version of the tool computes the minimum and maximum data age of the cause-effect chains in the system. This is done without prior knowledge of the concrete schedule, hence the analysis can already be applied in early design phases.
In addition, the tool generates job-level dependencies to restrict the data propagation between tasks such that the possible end-to-end latency does not exceed the specified age constraint.
Both, the analysis and the synthesis of job-level dependencies are described in http://www.es.mdh.se/publications/4368- ... ect_Chains.

Distribution:
The tool and the case study are available at: http://www.mechaniser.com

Statistics: Posted by MatthiasBecker — Mon Jul 11, 2016

Schedulability and response time analysis • RTMemController - WCET and ACET Analysis Tool for Memory Transactions

2015-07-29T09:22:40+01:00

Description of the tool:
RTMemController is an open-source tool for evaluating the worst-case and average-case execution time of memory transactions, which are executed by dynamically scheduling memory commands to DDR3 SDRAMs. The tool is based on the memory controller architecture and dynamic command scheduling algorithm proposed by Li et al. in ECRTS 2015. It uses the mathematical formalization of the algorithm, which has been shown to produce cycle-accurate timing behavior to a memory controller implementation in SystemC.

RTMemController includes two parts, a memory trace processor and a command scheduler. The former is responsible for providing a memory trace that contains the transactions executed by the memory controller. The memory trace is generated by combining a number of memory traces that are specified by the user. Each of them represents the memory trace from a requestor, e.g., a processor, a DMA controller or an accelerator, etc. The scheduler then executes the transactions by dynamically scheduling commands according to the proposed algorithm in our paper, which respects the timing constraints in the JEDEC DDR3 standard.

The memory trace processor reads the memory traces specified by the user, each of which represents a requestor that produces memory transactions. This tool uses a simple first-come first-serve (FCFS) mechanism. As a result, the memory trace processor combines the specified traces into a single combined trace according to the time stamps of the transactions. The generated memory trace provides a list of transactions that are executed by the memory controller. For users’ preferring arbitration policies for transactions from different requestors, it is easy to replace this memory trace processor with their own traces.

The command scheduler executes each transaction by dynamically scheduling memory commands subject to relevant DDR3 timing constraints. The scheduler collects the actual execution time of each transaction based on the command scheduling results. The maximum execution time of a transaction with particular size is collected as the measured WCET. Moreover, the worst-case execution time (WCET) of a transaction is given by two methods.

Key uses for this tool:
- Determine the actual and worst-case execution time of a trace that accesses a DDR3 memory.
- Determine the WCET of a memory transaction with a given size in a DDR3 memory.

People:
- Yonghui Li (Eindhoven University of Technology)
- Benny Akesson (CISTER/INESC TEC and ISEP, formerly CTU Prague)
- Kees Goossens (Eindhoven University of Technology)

Links:
- Website: http://www.es.ele.tue.nl/rtmemcontroller/

Release history:
- v1.2 was released on Jan. 21, 2015. The result folder is automatically created, such that users can easily find the results of worst-case and average-case execution times.
- v1.1 was released on Oct. 29, 2014. A memory bug was fixed based on the feedback from Danlu Guo who is working at the University of Waterloo. The feedback is appreciated!
- v1.0 was released on May. 8, 2014. This was the first public release of the tool.

Statistics: Posted by benny_akesson — Wed Jul 29, 2015

Schedulability and response time analysis • MPS-CAN Analyzer: A tool for end-to-end delay analysis of heterogeneous automotive networks

2015-07-07T10:48:21+01:00

Tool Description
MPS-CAN Analyzer is a response time analyzer for Mixed Periodic and Sporadic messages in Controller Area Network (CAN). It implements a number of response-time analyses for CAN addressing various queueing policies, buffer limitations in the CAN controllers, and various transmission modes implemented by higher-level protocols for CAN. It also integrates the response-time analysis for Ethernet AVB and CAN-to-Ethernet AVB Gateway. Hence, the tool is able to calculate the end-to-end delays of global messages that are originated in CAN network and are traversed through the gateway to the AVB network.

Distribution:
The tool and several test cases are available at: https://github.com/saadmubeen/MPS-CAN

Statistics: Posted by saad.mubeen — Tue Jul 07, 2015

Schedulability and response time analysis • RTC: Real-Time Calculus Toolbox

2015-06-24T16:12:07+01:00

Description of the tool
The Real-Time Calculus (RTC) Toolbox is a free Matlab toolbox for system-level performance analysis of distributed real-time and embedded systems.
The RTC Toolbox is based on an efficient representation of Variability Characterization Curves (VCC's) and implements most min-plus and max-plus algebra operators for these curves. On top of the min-plus and max-plus algebra operators, the RTC Toolbox provides a library of functions for Modular Performance Analysis with Real-Time Calculus.

Usage scope
To be defined

People
- Ernesto Wandeler
- Lothar Thiele

Links
- Website: website

Work in progress
2015.06.30 - To be defined

Release history
2011.03.07 - Tutorial extended.
2011.01.31 - Extensions to analyze multicore systems.
2010.07.26 - Interface to SymTA/S analysis tool.
2010.07.26 - Extensions for structured event streams.
2009.01.30 - BugFix and Update released.
2008.12.23 - Beta Version 1.2 released.
2008.10.14 - BugFix released.
2008.07.16 - BugFix released.
2008.05.30 - BugFix released.
2008.02.06 - BugFix released.
2007.09.24 - New components and tutorial.
2007.07.05 - BugFix released.
2007.06.25 - BugFix released.
2007.06.21 - New Version released.
2007.03.21 - BugFix released.
2006.10.02 - New tutorials and Java API released.
2006.10.02 - BugFix released.
2006.04.04 - First tutorial published.
2006.02.27 - First official beta version released.

Statistics: Posted by ggiannopoulou — Wed Jun 24, 2015

Schedulability and response time analysis • MAST: Modeling and Analysis Suite for Real-Time Applications

2015-06-24T11:22:09+01:00

Description of the tool
MAST is an open-source suite of tools to perform schedulability analysis of real-time distributed systems that assesses a rich variety of timing requirements. Via sensitivity analysis, you will know how far or close the system is from meeting those requirements. MAST uses a versatile and composable input model for the real-time behavior of the modules and platforms that form your system.

Usage scope
To be defined

People
- Michael González Harbour (Universidad de Cantabria)

Links
- Website: http://mast.unican.es/

Work in progress
2015.06.30 - To be defined

Release history
2015.06.30 - To be defined

Statistics: Posted by medinajl — Wed Jun 24, 2015

Schedulability and response time analysis • pyCPA: a pragmatic Python implementation of Compositional Performance Analysis

2015-06-23T14:03:12+01:00

Description of the tool
Given, you have a (distributed) real-time system and you want to know about worst-case (end-to-end) timing behavior, then you can use pyCPA to obtain these bounds.

You provide your architecture in the form of resources such as busses and CPUs and the corresponding scheduling policies. In a second step, you define your task-graph which is a specification of task-communication (precedence relations) and tasks' properties (best/worst-case execution times, activation patterns).

pyCPA will then calculate the following metrics:

worst-case response times (wcrt) of tasks
end-to-end timing of task chains
backlog of task activations (maximum buffer sizes)
output event models of dependent tasks

Usage scope
pyCPA is a pragmatic Python implementation of Compositional Performance Analysis (aka the SymTA/S approach provided by Symtavision) used for research in worst-case timing analysis. Unlike the commercial SymTA/S tool, pyCPA is not intended for commercial-grade use and does not guarantee correctness of the implementation.

The aim of pyCPA is also to provide an analysis core that can be easily extended (e.g. schedulers, activation patterns, etc.).

People
- Philip Axer (TU Braunschweig, NXP)
- Jonas Diemer (TU Braunschweig, Symtavision)
- Daniel Thiele (TU Braunschweig)
- Johannes Schlatow (TU Braunschweig)

Links
- Website: http://pycpa.readthedocs.org
- Code: bitbucket repository

Release history
Continuous release and improvement of the core.

Statistics: Posted by jschlatow — Tue Jun 23, 2015