Low Contention Mapping of RT Tasks onto a TilePro 64 Core Processor

1. Low Contention Mapping of RT Tasks onto a TilePro 64 Core Processor 1 Background Introduction = why 2 Goal 3 What 4 How 5 Experimental Result 6 Advantage & Limitaion 7 Significance & Improvement Lei Cui

2. Related Terms & Concepts • Predictability • TilePro 64-Core Processor • Contention • Static Timing analysis • NoC • IPC • Full-deplex (communication) • Jitter • Hyper-Period

3. 1 Background Introduction (why) • The predictability property of task execution is very important in the RT system, especially the RT tasks, in addition, its upper bound of execution times can be determined via static timing analysis. This method may result in the unsafe underestimations under a situation that when the underlying communication paths are not determined, that is, when data from multiple sources share parts of a routing path in the NoC, which can lead to a thing to happen---contention. • Therefore, the contention analysis is a must to guarantee to provide a safe and reliable bounds. At the same time, the paper takes a measure of utilizing a multi-core architecture to achieve mapping tasks to cores in such a way the contention is minimized. In addition, the less is the number of cores, the more possible the overhead incurs under the situation of IPC. • In addition, the contention will lead to the latency, and then lead to unsafe underestimation, and then lead to unpredictability.

4. 1 Background Introduction (con) Example: two messages 38 and 42 sent at the same time Effect: The contention on the link 45 is resulted, and then result in delay, and then latency, and then missed deadline, and then unbounded time, and then unpredictability, and then non-RT Drawback 1) The exhaustive approaches do not scale beyond small NoC mesh sizes as they can take days to solve mapping layouts. 2) Previous work viewed communication as temporally stateless, which limited the amount of communication that could feasibly be solved. 3) It also resulted in solutions that were overly conservative in that any potential for common message routes were considered contention. Improvement 1) by separating temporally disjoint messages when analyzing link contention scenarios and thus increasing communication predictability.

5. 2 Goal • Increase the predictability of RT tasks on NoC architectures • Models & Solutions to low or minimize contention during communications.

6. 3 What (Contributions) • Exhaustive Solver Modelexhaustively maps RT tasks onto cores to minimize contention and improve predictability • SBTFto map communication traces into time frames to ensure separation of analysis for temporally disjoint communication • Heuristic Model, HSolver for rapid discovery of low contention solutions

7. 4 How – SBTF (Software-Based Temporal Framing) Temporal Framing 9

8. 4 How – Exhaustive Solver Model

9. 4 How-Exhaustive Solver Model(continue) For example:

10. 4 How – Heuristic Model (Hsolver)

11. 4 How – Heuristic Model (Hsolver-con) Example: Maximum Cross Chat First (TMH) Degree(8) = 4, Degree(6) = 4 ==> 8,6 map empty cores (Group 1) Degree(3) = 3, Degree(4) = 3 ==> 3,4 map empty cores (Group 2) Degree(7) = 2, Degree(1) = 2 ==> 7,1 map empty cores (Group 3) Degree(5) = 1, Degree(2) = 1 ==> 5,2 map empty cores (Group 4) Degree(0) = 0 ==> 0 map empty cores (Group 5) Task Scheduling Sequence is 8, 6, (6,8). 3, 4, (4, 3), 7, 1, (1, 7), 5, 2, (2, 5), 0 Here final choose sequence: 8, 6, 3, 4, 7, 1, 5, 2, 0 Maximum Cross Chat First (CMH) Task Core

12. 5 Experimental Result (Ex 1) The 1st experiment compares the minimum solutions for each of the solvers as the complexity of the systems increase. This experiment evaluates the minimum aggregate cost across 100 randomly generated task sets in naive, heuristic and exhaustive model mappings as the NoC size increases along with a linear increase in the number of messages.

13. 5 Experimental Result (Ex 2) The 2nd experiment is to evaluate the HSolver approach to determine the rate at which heuristics were used to generate the low-cost solution. Percent Use of Core Selection Strategies Percent Use of Task Selection Strategies The left result shows the core selection strategies and the percent of use of each during heuristic solving, and a significant variation in the effectiveness of core strategies. Overall, minimizing the distances between frequently communicating cores is the most beneficial heuristic. The right picture shows that correlates well with the results where two selection strategies account for 98% of the low-cost solutions. The most effective solution is generally obtained by selecting tasks by Maximum Cross-Chat relative to the currently mapped tasks.

14. 5 Experimental Result (Ex 3) The 3rdexperiment assesses the impact of link contention on communication jitter. X-axis represents the 10 randomly generated task sets, each of which contains 200 messages within their hyper-period; Y-axis represents the standard deviation in clock cycles for different tasks sets for the three mapping approaches. This figure shows that any single contended link can have a significant impact on the standard deviation of transfer latencies. Table shows the timing results for each configuration evaluated in this experiment, all results determined by the heuristic approach converged within a second. Using the exhaustive solver, convergence can take up to 70 of minutes for solutions with contention.

15. 5 Experimental Result (Ex 4) The 4th experiments illustrate the impact of unavoidable contention on real-time predictability. These pictures depict the cost for sends and receives for one-to-one and two-to-one pairing of senders/receivers This experiment shows the worst-case experienced over multiple runs and emphasises the significant impact that contention can have on bounding WCET.

16. 6 Focus-on & Improvement

17. 7 Significance • 1) the first work to consider IPC for WC time frames to simplify analysis and to measure the impact an actual hardware for NoC-based real-time multi-core systems. • 2) the first work to address predictability of NoC communication via framing messages into temporal windows for real-time tasks.

18. Question • Experiment 3 • Experiment 4