TGFF Packed Schedules

(Note that some new options may not be compatible with this feature.)

Packed schedules are a recently added feature of TGFF. There are no guarantees of feasibility for the graphs generated with TGFF's default settings. There may exist a path through the task graph with a deadline which is tighter than the minimal sums of execution times and communication times for the tasks and communication events along that path. Using this system, a guarantee of feasibility (on an unlimited number of resources) could be given if one set deadlines on tasks based on the depth and the sum of average and multiple for the tasks (this is not an option in the code as provided). Of course, these deadlines may be extremely generous and it most cases generated in this way would likely fall into the easy to solve category. Thus, a new generation technique has been developed.

The aims for the packed schedule TGFF generator are:

  1. Generate difficult, but feasible, task-graph problem instances.
  2. Generate instances which include the realistic effects of communication (including resource contention, allowing overlapped computation and communication)
  3. Allow user controls of many types, including how difficult to make the instance; the number of processors and communication resources for which a known solution exists; level of heterogeneity (including generation of homogeneous cases); etc.
  4. Generation targeted to both preemptive and non-preemptive schedules for task execution and priority-based or arbitrated communication styles. This goal in particular is not yet fulfilled (see below).
  5. Creation of both periodic and non-periodic cases; this too has not been met, only a non-periodic instance has been developed.

With these goals in mind, the technique discussed below has been implemented (but is not yet available--please feel free to comment!). A new command pack_schedule has been created, it has the following 10 parameters:

-- (integer) the number of task graphs that the instance will have.
-- (integer) the average number of tasks expected for each processor in a known solution.
-- (float) average task execution time
-- (float) task time multiplier (most tasks are in the range [avg_task_time - mul_task_time, avg_task_time + mul_task_time]).
-- (float) amount of time usually between tasks executions on a resource
-- (float) rounding of task execution times (0 = no rounding, 1.0 = integer rounding).
-- (integer) the number of different types of PEs to generate. Note that the PE attributes are: cost, n_interrupts, and interrupt_time (the latter two of these set to 0). The type table is a list of task execution times. Cost is randomly selected from the [50,150] range. All tasks are unique.
- (integer) the number of PEs, which are randomly selected instances of the PE types, for which a solution is known.
-- (integer) the number of different types of COM resources; attributes are: cost, n_interrupts (= 0 for now), interrupt_time (also = 0 for now), datarate, n_connects (= 8 now), and code (= 2 now). The datarate is randomly selected from the range [50,150]. There is no type table for COM resources (arc data sizes are placed in a separate table).
-- (integer) the number of COM resources, which are randomly selected instances of the COM types, for which a solution is known.

In addition to these parameters, which are directly part of the pack_schedule directive, the method makes use of: seed and task_degree settings (more later).

Using these parameters the following generation algorithm has been devised.

  1. Randomly select PE and COM types for a solution instance. Generate a ArcDataSizeTable which will define the size of data communications for each arc in the task-graphs.
  2. Set the period of all tasks to: avg_tasks_per_pe * (avg_task_time + mul_task_time).
  3. For each PE instance in the solution, pack it's schedule. This entails generating tasks of random sizes within the [avg_task_time - mul_task_time, avg_task_time + mul_task_time] time range and placing them task_slack apart. Note that a final task may be placed which is shorter than this if space (before the supposed period) on the resource does not allow.
  4. Randomly assign the tasks generated across all the solution PEs to task graphs (there are num_task_graphs of them).
  5. For each pair of tasks, say t1 and t2, within the same task-graph where t1.end is less than t2.start consider addition of an arc. If t1 and t2 are assigned to the same PE in the known solution, then an arc with any datasize is possible since we assume that communication of data on the same resource takes no time (or could be included in the task time). However, generating relatively large data sizes for these arcs would be a clue to co-design tools to place the sending and receiving tasks on the same resource, thus these arcs are sized to be require on average 20% of avg_task_time assuming that the datarate of the first COM resource type is used. This arc-data-size is written to the ArcDataSizeTable. When t1 and t2 are assigned to different resources (PEs) then a check is made across the COM types in the solution for the largest amount of continuous open space between the time points t1 and t2. Using this open space computation, the associated size of the data for each COM resource is computed (using its datarate). The COM which supports the maximum arc-data-size--assuming it's greater then 1--is chosen for the solution, this usage of this COM is noted for subsequent COM scheduling and this arc-data-size is written to the ArcDataSizeTable. Note that arc generation may be limited by using the task_degree settings, otherwise arcs are generated without regard to maximum fan-in/out restrictions.
  6. The execution times for each task on each PE type is generated. If the PE type is the one for which the task runs on in the generated solution instance, then the solution instance time is used for this task on that PE, otherwise a value of 2 times this is used.
  7. Deadlines are placed on all terminal tasks which coincide exactly with that of the solution. Terminal tasks are those which have no children. Note that an alternative idea would be to put deadlines on all tasks in accordance with the known solution, however, this would also be strong guidance for co-design tools!
  8. Generate the TGFF file and the .eps file, include the known solution in the .tgff file as comments.

Our comments:

  1. This is a very heterogeneous problem...note in particular that there is no guarantee that increasing the cost of a processor (PE) generally speeds up tasks. In fact, cost/execution time is generally uncorrelated. This aspect is bothersome.
  2. A similar concern exists for COM resources, that is right now their datarate and cost are (statistically) unrelated. Also, the method used to create arcs, i.e., fill the largest space on COM resources seems to generate cases with high communication utilization. That is, the method will tend to fill the task_slack timeframes with communications; perhaps an arc_factor parameter restricted to the range (0,1) should be added to allow the user to help avoid this? Comments?
  3. An unscrupulous co-design researcher can cheat by merely looking for the PEs for which task times are 1/2 of others and generate the known correct allocation (although without looking at the commented solution one would not know the number of instances to use of a particular PE type). Of course, the 2X thing must be randomized, to perhaps give 1X to 2X ranges, but even this is with its limits. That is, since it is always more than 1X, a known solution exists for which the fastest PE for each task should be chosen. If instead a 0.5X to 2X range were used, then this might make for problem instances that are too easy. Again, any suggestions?
  4. The deadlines are generated tight against the known solution, but the user can use the task_slack parameter to help to loosen schedules. However, inter-task slack time tends to get filled with communications as mentioned above.
  5. Note that unlike using the other task-graph generation method, the pack_schedule generation algorithm does not guarantee fully connected task-graphs. The construction method otherwise used in TGFF generates fully connected task-graphs by construction, but here a single task-graph may be composed of several unconnected pieces.
  6. There is still some thought which should be given to the general nature of these instances w/r/t to local priority scheduling of PEs, i.e., we would like cases where many tasks simultaneously reach their ready state and where priority scheduling of these becomes important.
  7. It is felt that, when using relatively low task_slack that rather hard allocation/scheduling problem instances will be generated by this method.

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