ComPAS 2018: StarPU Tutorial - Toulouse, July 2018

Table of Contents

Other materials (talk slides, links) are available at the bottom of this page.


Connection to the Platform

You should have received information on how to connect to the platform.

Tutorial Material

All files needed for the lab works are available on the machine in the directory /mnt/n7fs/ens/tp_abuttari/TP_StarPU/material. A Tar archive is also available here: material.tar.

The following variables need to be set to use StarPU.

export TP_DIR=/mnt/n7fs/ens/tp_abuttari/TP_StarPU/

export HWLOC_PATH=$TP_DIR/hwloc-1.11.10

export FXT_PATH=$TP_DIR/fxt-0.3.8
export PATH=$FXT_PATH/bin:$PATH

export STARPU_PATH=$TP_DIR/starpu
export STARPU_IDLE_FILE=$HOME/starpu_idle_microsec.log


You can either add the previous lines to your file $HOME/.bash_profile, or use the script file /mnt/n7fs/ens/tp_abuttari/TP_StarPU/

Testing the installation

source /mnt/n7fs/ens/tp_abuttari/TP_StarPU/

You will find a copy of the script in /mnt/n7fs/ens/tp_abuttari/TP_StarPU/ To execute the script, simply call:

Note that the first time starpu_machine_display is executed, it calibrates the performance model of the bus, the results are then stored in different files in the directory $HOME/.starpu/sampling/bus.

Of course, on a heterogeneous cluster, the cluster launcher script should set various hostnames for the different node classes, as appropriate.

Session Part 1: Task-based Programming Model

Application Example: Vector Scaling

Making it and Running it

A typical Makefile for applications using StarPU is the following:

CFLAGS += $(shell pkg-config --cflags starpu-1.3)
LDFLAGS += $(shell pkg-config --libs starpu-1.3)
	nvcc $(CFLAGS) $< -c $

vector_scal_task_insert: vector_scal_task_insert.o vector_scal_cpu.o vector_scal_cuda.o vector_scal_opencl.o

Here are the source files for the application:

Run make, and run the resulting vector_scal_task_insert executable using the given script It should be working: it simply scales a given vector by a given factor.

source /mnt/n7fs/ens/tp_abuttari/TP_StarPU/

make vector_scal_task_insert


Computation Kernels

Examine the source code, starting from vector_scal_cpu.c : this is the actual computation code, which is wrapped into a vector_scal_cpu function which takes a series of DSM interfaces and a non-DSM parameter. The code simply gets the factor value from the non-DSM parameter, an actual pointer from the first DSM interface, and performs the vector scaling.

The GPU implementation, in, is basically the same, with the host part (vector_scal_cuda) which extracts the actual CUDA pointer from the DSM interface, and passes it to the device part (vector_mult_cuda) which performs the actual computation.

The OpenCL implementation in vector_scal_opencl.c and more hairy due to the low-level aspect of the OpenCL standard, but the principle remains the same.

Modify the source code of the different implementations (CPU, CUDA and OpenCL) and see which ones gets executed. You can force the execution of one the implementations simply by disabling a type of device when running your application, e.g.:

# to force the implementation on a GPU device, by default, it will enable CUDA
STARPU_NCPUS=0 vector_scal_task_insert

# to force the implementation on a OpenCL device
STARPU_NCPUS=0 STARPU_NCUDA=0 vector_scal_task_insert

You can set the environment variable STARPU_WORKER_STATS to 1 when running your application to see the number of tasks executed by each device. You can see the whole list of environment variables here.

STARPU_WORKER_STATS=1 vector_scal_task_insert

Main Code

Now examine vector_scal_task_insert.c: the cl (codelet) structure simply gathers pointers on the functions mentioned above.

The main function

  • Allocates an vector application buffer and fills it.
  • Registers it to StarPU, and gets back a DSM handle. From now on, the application is not supposed to access vector directly, since its content may be copied and modified by a task on a GPU, the main-memory copy then being outdated.
  • Submits a (asynchronous) task to StarPU.
  • Waits for task completion.
  • Unregisters the vector from StarPU, which brings back the modified version to main memory.

Data Partitioning

In the previous section, we submitted only one task. We here discuss how to partition data so as to submit multiple tasks which can be executed in parallel by the various CPUs and GPUs.

Let's examine mult.c.

  • The computation kernel, cpu_mult is a trivial matrix multiplication kernel, which operates on 3 given DSM interfaces. These will actually not be whole matrices, but only small parts of matrices.
  • init_problem_data initializes the whole A, B and C matrices.
  • partition_mult_data does the actual registration and partitioning. Matrices are first registered completely, then two partitioning filters are declared. The first one, vert, is used to split B and C vertically. The second one, horiz, is used to split A and C horizontally. We thus end up with a grid of pieces of C to be computed from stripes of A and B.
  • launch_tasks submits the actual tasks: for each piece of C, take the appropriate piece of A and B to produce the piece of C.
  • The access mode is interesting: A and B just need to be read from, and C will only be written to. This means that StarPU will make copies of the pieces of A and B along the machines, where they are needed for tasks, and will give to the tasks some uninitialized buffers for the pieces of C, since they will not be read from.
  • The main code initializes StarPU and data, launches tasks, unpartitions data, and unregisters it. Unpartitioning is an interesting step: until then the pieces of C are residing on the various GPUs where they have been computed. Unpartitioning will collect all the pieces of C into the main memory to form the whole C result matrix.

Run the application with the script, enabling some statistics:

Figures show how the computation were distributed on the various processing units.

Other example

gemm/xgemm.c is a very similar matrix-matrix product example, but which makes use of BLAS kernels for much better performance. The mult_kernel_common functions shows how we call DGEMM (CPUs) or cublasDgemm (GPUs) on the DSM interface.

Let's execute it.

source /mnt/n7fs/ens/tp_abuttari/TP_StarPU/

make gemm/sgemm
STARPU_WORKER_STATS=1 ./gemm/sgemm


More Advanced Examples

examples/lu/xlu_implicit.c is a more involved example: this is a simple LU decomposition algorithm. The dw_codelet_facto_v3 is actually the main algorithm loop, in a very readable, sequential-looking way. It simply submits all the tasks asynchronously, and waits for them all.

examples/cholesky/cholesky_implicit.c is a similar example, but which makes use of the starpu_insert_task helper. The _cholesky function looks very much like dw_codelet_facto_v3 of the previous paragraph, and all task submission details are handled by starpu_insert_task.

Thanks to being already using a task-based programming model, MAGMA and PLASMA have been easily ported to StarPU by simply using starpu_insert_task. #+end_comment


Take the vector example again, and add partitioning support to it, using the matrix-matrix multiplication as an example. Here we will use the starpu_vector_filter_block() filter function. You can see the list of predefined filters provided by StarPU here. Try to run it with various numbers of tasks.

Session Part 2: Optimizations

This is based on StarPU's documentation optimization chapter

Data Management

We have explained how StarPU can overlap computation and data transfers thanks to DMAs. This is however only possible when CUDA has control over the application buffers. The application should thus use starpu_malloc() when allocating its buffer, to permit asynchronous DMAs from and to it.

Take the vector example again, and fix the allocation, to make it use starpu_malloc().

Task Submission

To let StarPU reorder tasks, submit data transfers in advance, etc., task submission should be asynchronous whenever possible. Ideally, the application should behave like that: submit the whole graph of tasks, and wait for termination.

Task Scheduling Policy

By default, StarPU uses the eager simple greedy scheduler. This is because it provides correct load balance even if the application codelets do not have performance models: it uses a single central queue, from which workers draw tasks to work on. This however does not permit to prefetch data, since the scheduling decision is taken late.

If the application codelets have performance models, the scheduler should be changed to take benefit from that. StarPU will then really take scheduling decision in advance according to performance models, and issue data prefetch requests, to overlap data transfers and computations.

For instance, compare the eager (default) and dmda scheduling policies:

STARPU_BUS_STATS=1 STARPU_WORKER_STATS=1 gemm/sgemm -x 1024 -y 1024 -z 1024


STARPU_BUS_STATS=1 STARPU_WORKER_STATS=1 STARPU_SCHED=dmda gemm/sgemm -x 1024 -y 1024 -z 1024

You can see most (all?) the computation have been done on GPUs, leading to better performances.

Try other schedulers, use STARPU_SCHED=help to get the list.

Also try with various sizes and draw curves.

You can also try the double version, dgemm, and notice that GPUs get less great performance.

Performance Model Calibration

Performance prediction is essential for proper scheduling decisions, the performance models thus have to be calibrated. This is done automatically by StarPU when a codelet is executed for the first time. Once this is done, the result is saved to a file in $STARPU_HOME for later re-use. The starpu_perfmodel_display tool can be used to check the resulting performance model.

$ starpu_perfmodel_display -l
file: &lt;starpu_sgemm_gemm.mirage&gt;
$ starpu_perfmodel_display -s starpu_sgemm_gemm
performance model for cpu_impl_0
# hash		size		flops		mean (us)	stddev (us)		n
8bd4e11d	2359296        	0.000000e+00   	1.848856e+04   	4.026761e+03   	12
performance model for cuda_0_impl_0
# hash		size		flops		mean (us)	stddev (us)		n
8bd4e11d	2359296        	0.000000e+00   	4.918095e+02   	9.404866e+00   	66

This shows that for the sgemm kernel with a 2.5M matrix slice, the average execution time on CPUs was about 18ms, with a 4ms standard deviation, over 12 samples, while it took about 0.049ms on GPUs, with a 0.009ms standard deviation. It is a good idea to check this before doing actual performance measurements. If the kernel has varying performance, it may be a good idea to force StarPU to continue calibrating the performance model, by using export STARPU_CALIBRATE=1

If the code of a computation kernel is modified, the performance changes, the performance model thus has to be recalibrated from start. To do so, use export STARPU_CALIBRATE=2

The performance model can also be drawn by using starpu_perfmodel_plot, which will emit a gnuplot file in the current directory.

Sessions Part 3: MPI Support

StarPU provides support for MPI communications. It does so in two ways. Either the application specifies MPI transfers by hand, or it lets StarPU infer them from data dependencies.

Manual MPI transfers

Basically, StarPU provides equivalents of MPI_* functions, but which operate on DSM handles instead of void* buffers. The difference is that the source data may be residing on a GPU where it just got computed. StarPU will automatically handle copying it back to main memory before submitting it to MPI.

ring_async_implicit.c shows an example of mixing MPI communications and task submission. It is a classical ring MPI ping-pong, but the token which is being passed on from neighbour to neighbour is incremented by a starpu task at each step.

This is written very naturally by simply submitting all MPI communication requests and task submission asynchronously in a sequential-looking loop, and eventually waiting for all the tasks to complete.

source /mnt/n7fs/ens/tp_abuttari/TP_StarPU/

make ring_async_implicit
mpirun -np 2 $PWD/ring_async_implicit


A stencil application shows a basic MPI task model application. The data distribution over MPI nodes is decided by the my_distrib function, and can thus be changed trivially. It also shows how data can be migrated to a new distribution.

source /mnt/n7fs/ens/tp_abuttari/TP_StarPU/

make stencil5
mpirun -np 2 $PWD/stencil5 -display


Session Part 4: OpenMP Support

The Klang-Omp OpenMP Compiler

The Klang-Omp OpenMP compiler converts C/C++ source codes annotated with OpenMP 4 directives into StarPU enabled codes. Klang-Omp is source-to-source compiler based on the LLVM/CLang compiler framework.

The following shell sequence shows an example of an OpenMP version of the Cholesky decomposition compiled into StarPU code.

source /gpfslocal/pub/training/runtime_june2016/openmp/environment
cp -r /gpfslocal/pub/training/runtime_june2016/openmp/Cholesky .
cd Cholesky

Homepage of the Klang-Omp OpenMP compiler: Klang-Omp



For any questions regarding StarPU, please contact the StarPU developers mailing list.

More Performance Optimizations

The StarPU documentation performance feedback chapter provides more optimization tips for further reading after this tutorial.


FxT Tracing Support

In addition to online profiling, StarPU provides offline profiling tools, based on recording a trace of events during execution, and analyzing it afterwards.

To use the version of StarPU compiled with FxT support, you need to reload the module StarPU after loading the module FxT.

module unload runtime/starpu/1.1.4
module load trace/fxt/0.2.13
module load runtime/starpu/1.1.4

The trace file is stored in /tmp by default. Since execution will happen on a cluster node, the file will not be reachable after execution, we need to tell StarPU to store output traces in the home directory, by setting:


do not forget the add the line in your file .bash_profile.

The application should be run again, and this time a prof_file_XX_YY trace file will be generated in your home directory. This can be converted to several formats by using:

$ starpu_fxt_tool -i ~/prof_file_*

This will create

  • a paje.trace file, which can be opened by using the ViTE tool. This shows a Gant diagram of the tasks which executed, and thus the activity and idleness of tasks, as well as dependencies, data transfers, etc. You may have to zoom in to actually focus on the computation part, and not the lengthy CUDA initialization.
  • a file, which contains the graph of all the tasks submitted by the application. It can be opened by using Graphviz.
  • an file, which records the activity of all processing units over time.


Other Materials: Talk Slides and Website Links

Author: root

Created: 2022-08-05 Fri 16:37