run_vtr_task

This script is used to execute one or more tasks (i.e. collections of benchmarks and architectures).

See also

See Tasks for creation and configuration of tasks.

This script runs the VTR flow for a single benchmark circuit and architecture file.

The script is located at:

$VTR_ROOT/vtr_flow/scripts/run_vtr_task.py

Basic Usage

Typical usage is:

run_vtr_task.py <task_name1> <task_name2> ...

Note

At least one task must be specified, either directly as a parameter or via the -l options.

Output

Each task will execute the script specified in the configuration file for every benchmark/circuit/option combination. The standard output of the underlying script will be forwarded to the output of this script.

If golden results exist (see parse_vtr_task), they will be inspected for runtime and memory usage.

Detailed Command-line Options

-s <script_param> ...

Treat the remaining command line options as parameters to forward to the underlying script (e.g. run_vtr_flow).

-j <N>

Perform parallel execution using N threads.

Note

Only effective for -system local

Warning

Large benchmarks will use very large amounts of memory (several to 10s of gigabytes). Because of this, parallel execution often saturates the physical memory, requiring the use of swap memory, which significantly slows execution. Be sure you have allocated a sufficiently large swap memory or errors may result.

-l <task_list_file>

A file containing a list of tasks to execute.

Each task name should be on a separate line, e.g.:

<task_name1>
<task_name2>
<task_name3>
...

-temp_dir <path>

Alternate directory for files generated by a set of tasks. The script will automatically create this directory if necessary.

Default: config/.. for each task being run

Specifies the parent directory for the output of this set of tasks, which will contain <task_name>/run<#> directories, as well as any generated parse results.

A task folder or list with a config directory must still be specified when invoking the script.

-system {local | scripts}

Controls how the actions (e.g. invocations of run_vtr_flow) are called.

Default: local

• local: Runs the flow invocations on the local machine (potentially in parallel with the -j option).

  Example:

  #From $VTR_ROOT/vtr_flow/tasks
  
  $ ../scripts/run_vtr_task.py regression_tests/vtr_reg_basic/basic_timing
  regression_tests/vtr_reg_basic/basic_timing: k6_N10_mem32K_40nm.xml/ch_intrinsics.v/common          OK              (took 2.24 seconds)
  regression_tests/vtr_reg_basic/basic_timing: k6_N10_mem32K_40nm.xml/diffeq1.v/common                OK              (took 10.94 seconds)
  

• scripts: Prints out all the generated script files (instead of calling them to run all the flow invocations).

  Example:

  #From $VTR_ROOT/vtr_flow/tasks
  
  $ ../scripts/run_vtr_task.py regression_tests/vtr_reg_basic/basic_timing -system scripts
  /project/trees/vtr/vtr_flow/tasks/regression_tests/vtr_reg_basic/basic_timing/run001/k6_N10_mem32K_40nm.xml/ch_intrinsics.v/common/vtr_flow.sh
  /project/trees/vtr/vtr_flow/tasks/regression_tests/vtr_reg_basic/basic_timing/run001/k6_N10_mem32K_40nm.xml/diffeq1.v/common/vtr_flow.sh
  

  Each generated script file (vtr_flow.sh) corresponds to a particular flow invocation generated by the task, and is located within its own directory.

  This list of scripts can be used to run flow invocations on different computing infrastructures (e.g. a compute cluster).

  Using the output of -system scripts to run a task

  An example of using the output would be:

  #From $VTR_ROOT/vtr_flow/tasks
  
  $ ../scripts/run_vtr_task.py regression_tests/vtr_reg_basic/basic_timing -system scripts | parallel -j4 'cd $(dirname {}) && {}'
  regression_tests/vtr_reg_basic/basic_timing: k6_N10_mem32K_40nm.xml/ch_intrinsics.v/common          OK              (took 2.11 seconds)
  regression_tests/vtr_reg_basic/basic_timing: k6_N10_mem32K_40nm.xml/diffeq1.v/common                OK              (took 10.94 seconds)
  

  where {} is a special variable interpretted by the parallel command to represent the input line (i.e. a script, see parallel’s documentation for details). This will run the scripts generated by run_vtr_task.py in parallel (up to 4 at-a-time due to -j4). Each script is invoked in the script’s containing directory (cd $(dirname {})), which mimics the behaviour of -system local -j4.

  Note

  While this example shows how the flow invocations could be run locally, similar techniques can be used to submit jobs to other compute infrastructures (e.g. a compute cluster)

  Determining Resource Requirements

  Often, when running in a cluster computing enviroment, it is useful to know what compute resources are required for each flow invocation.

  Each generated vtr_flow.sh scripts contains the expected run-time and memory use of each flow invocation (derived from golden reference results). These can be inspected to determine compute requirements:

  $ grep VTR_RUNTIME_ESTIMATE_SECONDS /project/trees/vtr/vtr_flow/tasks/regression_tests/vtr_reg_basic/basic_timing/run001/k6_N10_mem32K_40nm.xml/ch_intrinsics.v/common/vtr_flow.sh
  VTR_RUNTIME_ESTIMATE_SECONDS=2.96
  
  $ grep VTR_MEMORY_ESTIMATE_BYTES /project/trees/vtr/vtr_flow/tasks/regression_tests/vtr_reg_basic/basic_timing/run001/k6_N10_mem32K_40nm.xml/ch_intrinsics.v/common/vtr_flow.sh
  VTR_MEMORY_ESTIMATE_BYTES=63422464
  

  Note

  If the resource estimates are unkown they will be set to 0