Task scheduler performance survey This dataset contains results of task graph scheduler performance survey. The results are stored in the following files, which correspond to simulations performed on the `elementary`, `irw` and `pegasus` task graph datasets published at https://doi.org/10.5281/zenodo.2630384. elementary-result.zip irw-result.zip pegasus-result.zip The files contain compressed pandas dataframes in CSV format, it can be read with the following Python code: ```python import pandas as pd frame = pd.read_csv("elementary-result.zip") ``` Each row in the frame corresponds to a single instance of a task graph that was simulated with a specific configuration (network model, scheduler etc.). The list below summarizes the meaning of the individual columns. graph_name - name of the benchmarked task graph graph_set - name of the task graph dataset from which the graph originates graph_id - unique ID of the graph cluster_name - type of cluster used in this instance the format is <number-of-workers>x<number-of-cores>; 32x16 means 32 workers, each with 16 cores bandwidth - network bandwidth [MiB] netmodel - network model (simple or maxmin) scheduler_name - name of the scheduler imode - information mode min_sched_interval - minimal scheduling delay [s] sched_time - duration of each scheduler invocation [s] time - simulated makespan of the task graph execution [s] execution_time - real duration of all scheduler invocations [s] total_transfer - amount of data transferred amongst workers [MiB] The file `charts.zip` contains charts obtained by processing the datasets. On the X axis there is always bandwidth in [MiB/s]. There are the following files: [DATASET]-schedulers-time - Absolute makespan produced by schedulers [seconds] [DATASET]-schedulers-score - The same as above but normalized with respect to the best schedule (shortest makespan) for the given configuration. [DATASET]-schedulers-transfer - Sums of transfers between all workers for a given configuration [MiB] [DATASET]-[CLUSTER]-netmodel-time - Comparison of netmodels, absolute times [seconds] [DATASET]-[CLUSTER]-netmodel-score - Comparison of netmodels, normalized to the average of model "simple" [DATASET]-[CLUSTER]-netmodel-transfer - Comparison of netmodels, sum of transfered data between all workers [MiB] [DATASET]-[CLUSTER]-schedtime-time - Comparison of MSD, absolute times [seconds] [DATASET]-[CLUSTER]-schedtime-score - Comparison of MSD, normalized to the average of "MSD=0.0" case [DATASET]-[CLUSTER]-imode-time - Comparison of Imodes, absolute times [seconds] [DATASET]-[CLUSTER]-imode-score - Comparison of Imodes, normalized to the average of "exact" imode Reproducing the results 1. Download and install Estee (https://github.com/It4innovations/estee) $ git clone https://github.com/It4innovations/estee $ cd estee $ pip install . 2. Generate task graphs You can either use the provided script `benchmarks/generate.py` to generate graphs from three categories (elementary, irw and pegasus): $ cd benchmarks $ python generate.py elementary.zip elementary $ python generate.py irw.zip irw $ python generate.py pegasus.zip pegasus or use our task graph dataset that is provided at https://doi.org/10.5281/zenodo.2630384. 3. Run benchmarks To run a benchmark suite, you should prepare a JSON file describing the benchmark. The file that was used to run experiments from the paper is provided in `benchmark.json`. Then you can run the benchmark using this command: $ python pbs.py compute benchmark.json The benchmark script can be interrupted at any time (for example using Ctrl+C). When interrupted, it will store the computed results to the result file and restore the computation when launched again. 3. Visualizing results $ python view.py --all <result-file> The resulting plots will appear in a folder called `outputs`.