| Taskflow: A General-purpose Task-parallel Programming System

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Benchmark Taskflow

# Compile and Run Benchmarks

To build the benchmark code, enable the CMake option `TF_BUILD_BENCHMARKS` to `ON` as follows:

# under /taskflow/build
~$ cmake ../ -DTF_BUILD_BENCHMARKS=ON
~$ make

After you successfully build the benchmark code, you can find all benchmark instances in the `benchmarks/` folder. You can run the executable of each instance in the corresponding folder.

~$ cd benchmarks & ls
bench_black_scholes bench_binary_tree bench_graph_traversal ...
~$ ./bench_graph_traversal
|V|+|E| Runtime
2 0.197
842 0.198
3284 0.488
7288 0.774
... ...
... ...
619802 75.135
664771 77.436
711200 83.957

You can display the help message by giving the option `--help`.

~$ ./bench_graph_traversal --help
Graph Traversal
Usage: ./bench_graph_traversal [OPTIONS]
Options:
-h,--help Print this help message and exit
-t,--num_threads UINT number of threads (default=1)
-r,--num_rounds UINT number of rounds (default=1)
-m,--model TEXT model name tbb|omp|tf (default=tf)

We currently implement the following instances that are commonly used by the parallel computing community to evaluate the system performance.

Instance | Description
--- | ---
bench_binary_tree | traverses a complete binary tree
bench_black_scholes | computes option pricing with Black-Shcoles Models
bench_graph_traversal | traverses a randomly generated direct acyclic graph
bench_linear_chain | traverses a linear chain of tasks
bench_mandelbrot | exploits imbalanced workloads in a Mandelbrot set
bench_matrix_multiplication | multiplies two 2D matrices
bench_mnist | trains a neural network-based image classifier on the MNIST dataset
bench_parallel_sort | sorts a range of items
bench_reduce_sum | sums a range of items using reduction
bench_wavefront | propagates computations in a 2D grid
bench_linear_pipeline | performs pipeline parallelism on a linear chain of pipes
bench_graph_pipeline | performs pipeline parallelism on a graph of pipes
bench_deferred_pipeline | performs pipeline parallelism with dependencies from future pipes
bench_data_pipeline | performs pipeline parallelisms on a cache-friendly data wrapper
bench_thread_pool | uses our executor as a simple thread pool
bench_for_each | performs parallel-iteration algorithms
bench_scan | performs parallel-scan algorithms
bench_async_task | creates asynchronous tasks
bench_fibonacci | finds Fibonacci numbers using recursive asynchronous tasking
bench_nqueens | parallelizes n-queen search using recursive asynchronous tasking
bench_integrate | parallelizes integration using recursive asynchronous tasking
bench_primes | finds a range of prime numbers using parallel-reduction algorithms
bench_skynet | traverses a 10-ray tree using recursive asynchronous tasking

# Configure Run Options

We implement consistent options for each benchmark instance. Common options are:

option | value | function
--- | --- | ---
`-h` | none | displays the help message
`-t` | integer | configures the number of threads to run
`-r` | integer | configures the number of rounds to run
`-m` | string | configures the baseline models to run, tbb, omp, or tf

You can configure the benchmarking environment by giving different options.

## Specify the Run Model

In addition to a Taskflow-based implementation for each benchmark instance, we have implemented two baseline models using the state-of-the-art parallel programming libraries, OpenMP and Intel TBB, to measure and evaluate the performance of Taskflow. You can select different implementations by passing the option `-m`.

~$ ./bench_graph_traversal -m tf # run the Taskflow implementation (default)
~$ ./bench_graph_traversal -m tbb # run the TBB implementation
~$ ./bench_graph_traversal -m omp # run the OpenMP implementation

## Specify the Number of Threads

You can configure the number of threads to run a benchmark instance by passing the option `-t`. The default value is one.

# run the Taskflow implementation using 4 threads
~$ ./bench_graph_traversal -m tf -t 4

Depending on your environment, you may need to use `taskset` to set the CPU affinity of the running process. This allows the OS scheduler to keep process on the same CPU(s) as long as practical for performance reason.

# affine the process to 4 CPUs, CPU 0, CPU 1, CPU 2, and CPU 3
~$ taskset -c 0-3 bench_graph_traversal -t 4

## Specify the Number of Rounds

Each benchmark instance evaluates the runtime of the implementation at different problem sizes. Each problem size corresponds to one iteration. You can configure the number of rounds per iteration to average the runtime.

# measure the %Taskflow runtime by averaging the results over 10 runs
~$ ./bench_graph_traversal -r 10 -m tf
|V|+|E| Runtime
2 0.109 # the runtime value 0.109 is an average of 10 runs
842 0.298
... ...
619802 73.135
664771 74.436