A Unified Runtime System for Heterogeneous Multicore Architectures


StarPU is a task programming library for hybrid architectures

  1. The application provides algorithms and constraints
    • CPU/GPU implementations of tasks
    • A graph of tasks, using either the StarPU's high level GCC plugin pragmas or StarPU's rich C API

  2. StarPU handles run-time concerns
    • Task dependencies
    • Optimized heterogeneous scheduling
    • Optimized data transfers and replication between main memory and discrete memories
    • Optimized cluster communications

Rather than handling low-level issues, programmers can concentrate on algorithmic concerns!

The StarPU documentation is available in PDF and in HTML. Please note that these documents are up-to-date with the latest release of StarPU.


November 2012 »  StarPU at SuperComputing'12: A StarPU poster is on display on the Inria booth, Feel free to come & have a chat, at booth #1209!

October 2012 » The v1.0.4 release of StarPU is now available!. This release mainly brings bug fixes.

September 2012 »  StarPU was presented at the conference EuroMPI

September 2012 » The v1.0.3 release of StarPU is now available!. This release mainly brings bug fixes.

August 2012 » The v1.0.2 release of StarPU is now available!. This release notably fixes CPU/GPU binding.

July 2012 »  StarPU was presented at the GNU Tools Cauldron 2012

Get the latest StarPU news by subscribing to the starpu-announce mailing list. See also the news archive.


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



Portability is obtained by the means of a unified abstraction of the machine. StarPU offers a unified offloadable task abstraction named codelet. Rather than rewriting the entire code, programmers can encapsulate existing functions within codelets. In case a codelet can run on heterogeneous architectures, it is possible to specify one function for each architectures (e.g. one function for CUDA and one function for CPUs). StarPU takes care of scheduling and executing those codelets as efficiently as possible over the entire machine, include multiple GPUs. One can even specify several functions for each architecture, and StarPU will automatically determine which version is best for each input size.

Data transfers

To relieve programmers from the burden of explicit data transfers, a high-level data management library enforces memory coherency over the machine: before a codelet starts (e.g. on an accelerator), all its data are automatically made available on the compute resource. Data are also kept on e.g. GPUs as long as they are needed for further tasks. When a device runs out of memory, StarPU uses an LRU strategy to evict unused data. StarPU also takes care of automatically prefetching data, which thus permits to overlap data transfers with computations (including GPU-GPU direct transfers) to achieve the most of the architecture.


Dependencies between tasks can be given several ways, to provide the programmer with best flexibility:

StarPU also supports an OpenMP-like reduction access mode.

Heterogeneous Scheduling

StarPU obtains portable performances by efficiently (and easily) using all computing resources at the same time. StarPU also takes advantage of the heterogeneous nature of a machine, for instance by using scheduling strategies based on auto-tuned performance models. These determine the relative performance achieved by the different processing units for the various kinds of task, and thus permits to automatically let processing units execute the tasks they are the best for.


To deal with clusters, StarPU can nicely integrate with MPI through explicit network communications, which will then be automatically combined and overlapped with the intra-node data transfers and computation. The application can also just provide the whole task graph, a data distribution over MPI nodes, and StarPU will automatically determine which MPI node should execute which task, and generate all required MPI communications accordingly.

Extensions to the C Language

StarPU comes with a GCC plug-in that extends the C programming language with pragmas and attributes that make it easy to annotate a sequential C program to turn it into a parallel StarPU program.

All in all

All that means that, with the help of StarPU's extensions to the C language, the following sequential source code of a tiled version of the classical Cholesky factorization algorithm using BLAS is also valid StarPU code, possibly running on all the CPUs and GPUs, and given a data distribution over MPI nodes, it is even a distribute version!

for (k = 0; k < tiles; k++) {
  for (m = k+1; m < tiles; m++)
    trsm(A[k,k], A[m,k])
  for (m = k+1; m < tiles; m++)
    syrk(A[m,k], A[m, m])
  for (m = k+1, m < tiles; m++)
    for (n = k+1, n < m; n++)
      gemm(A[m,k], A[n,k], A[m,n])

Supported Architectures

and soon

Supported Operating Systems

Performance analysis tools

In order to understand the performance obtained by StarPU, it is helpful to visualize the actual behaviour of the applications running on complex heterogeneous multicore architectures. StarPU therefore makes it possible to generate Pajé traces that can be visualized thanks to the ViTE (Visual Trace Explorer) open source tool.

Example: LU decomposition on 3 CPU cores and a GPU using a very simple greedy scheduling strategy. The green (resp. red) sections indicate when the corresponding processing unit is busy (resp. idle). The number of ready tasks is displayed in the curve on top: it appears that with this scheduling policy, the algorithm suffers a certain lack of parallelism. Measured speed: 175.32 GFlop/s

LU decomposition (greedy)

This second trace depicts the behaviour of the same application using a scheduling strategy trying to minimize load imbalance thanks to auto-tuned performance models and to keep data locality as high as possible. In this example, the Pajé trace clearly shows that this scheduling strategy outperforms the previous one in terms of processor usage. Measured speed: 239.60 GFlop/s

LU decomposition (dmda)

Software using StarPU

Some software is known for being able to use StarPU to tackle heterogeneous architectures, here is a non-exhaustive list:

You can find below the list of publications related to applications using StarPU.


All StarPU related publications are also listed here with the corresponding Bibtex entries.

A good overview is available in the following Research Report.

General presentations

  1. C. Augonnet, S. Thibault, R. Namyst, and P.-A. Wacrenier.
    StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures. Concurrency and Computation: Practice and Experience, Special Issue: Euro-Par 2009, 23:187-198, February 2011.
    Available here.
  2. C. Augonnet.
    StarPU: un support exécutif unifié pour les architectures multicoeurs hétérogènes. In 19èmes Rencontres Francophones du Parallélisme, September 2009. Note: Best Paper Award.
    Available here. (French version)
  3. C. Augonnet, S. Thibault, R. Namyst, and P.-A. Wacrenier.
    StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures. In Proceedings of the 15th International Euro-Par Conference, volume 5704 of LNCS, August 2009.
    Available here. (short version)
  4. C. Augonnet and R. Namyst.
    A unified runtime system for heterogeneous multicore architectures. In Proceedings of the International Euro-Par Workshops 2008, HPPC'08, volume 5415 of LNCS, August 2008.
    Available here. (early version)

On MPI support

  1. C. Augonnet, O. Aumage, N. Furmento, R. Namyst, and S. Thibault.
    StarPU-MPI: Task Programming over Clusters of Machines Enhanced with Accelerators. In EuroMPI 2012, volume 7490 of LNCS, September 2012. Note: Poster Session.
    Available here.

On data transfer management

  1. C. Augonnet, J. Clet-Ortega, S. Thibault, and R. Namyst
    Data-Aware Task Scheduling on Multi-Accelerator based Platforms. In The 16th International Conference on Parallel and Distributed Systems (ICPADS), December 2010.
    Available here.

On performance model tuning

  1. C. Augonnet, S. Thibault, and R. Namyst.
    Automatic Calibration of Performance Models on Heterogeneous Multicore Architectures. In Proceedings of the International Euro-Par Workshops 2009, HPPC'09, volume 6043 of LNCS, August 2009.
    Available here.

On the Cell support

  1. C. Augonnet, S. Thibault, R. Namyst, and M. Nijhuis.
    Exploiting the Cell/BE architecture with the StarPU unified runtime system. In SAMOS Workshop - International Workshop on Systems, Architectures, Modeling, and Simulation, volume 5657 of LNCS, July 2009.
    Available here.

On Applications

  1. S.A. Mahmoudi, P. Manneback, C. Augonnet, and S. Thibault.
    Traitements d'Images sur Architectures Parallèles et Hétérogènes. Technique et Science Informatiques, 2012.
    Available here.
  2. S. Benkner, S. Pllana, J.L. Träff, P. Tsigas, U. Dolinsky, C. Augonnet, B. Bachmayer, C. Kessler, D. Moloney, and V. Osipov.
    PEPPHER: Efficient and Productive Usage of Hybrid Computing Systems. IEEE Micro, 31(5):28-41, September 2011.
    Available here.
  3. U. Dastgeer, C. Kessler, and S. Thibault.
    Flexible runtime support for efficient skeleton programming on hybrid systems. In Proceedings of the International Conference on Parallel Computing (ParCo), Applications, Tools and Techniques on the Road to Exascale Computing, volume 22 of Advances of Parallel Computing, August 2011.
    Available here.
  4. S. Henry.
    Programmation multi-accélérateurs unifiée en OpenCL. In 20èmes Rencontres Francophones du Parallélisme (RenPar'20), May 2011.
    Available here.
  5. S.A. Mahmoudi, P. Manneback, C. Augonnet, and S. Thibault.
    Détection optimale des coins et contours dans des bases d'images volumineuses sur architectures multicoeurs hétérogènes. In 20èmes Rencontres Francophones du Parallélisme, May 2011.
    Available here.
  6. E. Agullo, C. Augonnet, J. Dongarra, H. Ltaief, R. Namyst, S. Thibault, and S. Tomov.
    A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs. In GPU Computing Gems, volume 2., September 2010.
    Available here.
  7. E. Agullo, C. Augonnet, J. Dongarra, M. Faverge, H. Ltaief, S. Thibault, and S. Tomov.
    QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators. In 25th IEEE International Parallel & Distributed Processing Symposium (IEEE IPDPS 2011), May 2011.
    Available here.
  8. E. Agullo, C. Augonnet, J. Dongarra, H. Ltaief, R. Namyst, J. Roman, S. Thibault, and S. Tomov.
    Dynamically scheduled Cholesky factorization on multicore architectures with GPU accelerators. In Symposium on Application Accelerators in High Performance Computing (SAAHPC), July 2010.
    Available here.
  9. E. Agullo, C. Augonnet, J. Dongarra, M. Faverge, J. Langou, H. Ltaief, and S. Tomov.
    LU factorization for accelerator-based systems. In 9th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 11), June 2011.
    Available here

Last updated on 2012/10/03.