/PAMPAR

PAMPAR - A new parallel benchmark for parallel programing interfaces.

Primary LanguageC

PAMPAR

To cite this work:

  • Garcia, A. M., Schepke, C., Girardi, A. PAMPAR: A new parallel benchmark for performance and energy consumption evaluation. Concurrency Computation Practice Experience. 2019;e5504. https://doi.org/10.1002/cpe.5504

In this project we aim to build a suite of parallel benchmarks to evaluate parallel programing interfaces (parallel models).

In our research we found out that there is no benchmark focused on comparing parallel programing interfaces.

This is a initial work and we still updating these applications. Currently, the suite consists of 13 parallel benchmarks (micro, kernels, and pseudo-applications), each parallelized using PThreads, OpenMP, MPI-1, and MPI-2 (dynamic processes creation). We also offer a clean serial version to be used as base result and to ease implementations using new programming models.

Table: Suite details.

Benchmark Acronym Set Complexity Domain
Pi Caluculation PI Micro O(n) Math
Dot Produtct DP Micro O(n) Linear Algebra
Numeric Integration NI Micro O(n) Physics
Odd-Even Sort OE Kernel O(n²) Sorting Algorithms
Harmonic Sums HA Kernel O(n×d) Physics, Engineering
Disc. Fourier Transf. DFT Kernel O(n²) Digital Signal Processing
Matrix Multiplication MM Kernel O(n³) Linear Algebra
Gram-Schmidt Process GS Kernel O(n³) Linear Algebra
Jacobi Method JA Kernel O(n³) Linear Algebra
Dijkstra Shortest Path DJ Kernel O(n³) Graphs
Turing Ring TR Pseudo-application O(m×n²) Chemistry, Biological Simulation
Histograms Similarity SH Pseudo-application O(n³) Image Processing, Pattern Recognition
Game of Life GL Pseudo-application O(n³) Cellular Automata, Physics Simulation

(GL-MPI and SH still need some polishing. Any help is welcome!)

The goal is to paralelize the set using more interfaces and add new benchmarks according to the demand.

More details and studies on these applications can be found at: https://www.researchgate.net/project/A-Parallel-Benchmark-for-Performance-Evaluation-and-Energy-Consumption-in-Multicore-and-Manycore-Architectures

How to run:

  • To compile all the pseudo-applications, go to the PAMPAR root directory and run:

    ~$ make

  • You can run the 'run.sh' script and follow the steps to setup and run the benchmark.

    ~$ bash run.sh

It will run the pseudo-applications according to the setup and give the execution time for each case.

How to run manually:

  • Compile the applications using the Makefile

  • To run a PThread or OpenMP application, run:

    ~$ ./<executable> <number_of_threads> <input_problem>

    Example: ~$ ./pthread 4 2048

  • To run a MPI-1 application, run:

    ~$ mpirun -np <number_of_processes> <executable> <input_problem>

    Example: ~$ mpirun -np 4 ./mpi1 2048

  • To run a MPI-2 application, run:

    ~$ mpirun -np 1 <executable> <number_of_child_processes> <input_problem> <child_executable>

    Example: ~$ mpirun -np 1 ./pai 3 2048 ./filho

Workload classes

PAMPAR has 3 classes of problems size and one aditional for debug purposes. The classes names are:

  • Small
  • Medium
  • Large
  • Debug

These classes can be selected in the menu when running the run.sh script.

Examples of <input_problem> parameters for each application using the "medium" class:

- DFT: 32368
-  DJ: 2048 adjacency_matrix.txt
-  DP: 15000000000
-  GS: 1024
-  HA: 100000 100000
-  JA: 2048
-  MM: 4096
-  NI: 1000000000
-  OE: 225000
-  PI: 4000000000
-  TR: 2048