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ffnamespace:architecture [2014/08/04 19:09] aldinuc |
ffnamespace:architecture [2014/08/14 02:33] aldinuc [High-level Patterns] |
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| FastFlow architecture is organised in three main tiers: | FastFlow architecture is organised in three main tiers: | ||
| - | - **High-level patterns** They are clearly characterised in a specific usage context and are targeted to the parallelisation of sequential (legacy) code. Examples are exploitation of loop parallelism, stream parallelism, data-parallel algorithms, execution of general workflows of tasks, etc. The are typically equipped with self-optimisation capabilities (e.g. load-balancing, grain auto-tuning, parallelism-degree auto-tuning) and exhibit no or limited nesting capability. Examples are: ''parallel-for'', ''pipeline'', ''stencil-reduce'', ''mdf'' (macro-data-flow). Some of them targets specific devices (e.g. GPGPUs). They are implemented on top of **core patterns**. | + | - **High-level patterns** They are clearly characterised in a specific usage context and are targeted to the parallelisation of sequential (legacy) code. Examples are exploitation of loop parallelism, stream parallelism, data-parallel algorithms, execution of general workflows of tasks, etc. They are typically equipped with self-optimisation capabilities (e.g. load-balancing, grain auto-tuning, parallelism-degree auto-tuning) and exhibit limited nesting capability. Examples are: ''parallel-for'', ''pipeline'', ''stencil-reduce'', ''mdf'' (macro-data-flow). Some of them targets specific devices (e.g. GPGPUs). They are implemented on top of **core patterns**. |
| - | - **Core patterns** They provide a general //data-centric// parallel programming model with its run-time support, which is designed to be minimal and reduce to the minimum typical sources of overheads in parallel programming. At this level there are two patterns (''farm'' and ''pipeline'') and one pattern-modifier (''loopback''). They make it possible to build very general (deadlock-free) cyclic process networks. They are not graphs of tasks, they are graphs of parallel executors (processes/threads). Tasks or data items flows across them. Overall, the programming model can be envisioned as a shared-memory streaming model, i.e. a shared-memory model equipped with message-passing synchronisations. They are implemented on top of **building blocks**. | + | - **Core patterns** They provide a general //data-centric// parallel programming model with its run-time support, which is designed to be minimal and reduce to the minimum typical sources of overheads in parallel programming. At this level there are two patterns (''farm'' and ''pipeline'') and one pattern-modifier (''feedback''). They make it possible to build very general (deadlock-free) cyclic process networks. They are not graphs of tasks, they are graphs of parallel executors (processes/threads). Tasks or data items flows across them. Overall, the programming model can be envisioned as a shared-memory streaming model, i.e. a shared-memory model equipped with message-passing synchronisations. They are implemented on top of **building blocks**. |
| - **Building blocks** It provides the basics blocks to build (and generate via C++ header-only templates) the run-time support of core patterns. Typical objects at this level are queues (e.g. wait-free fence-free SPSC queues, bound and unbound), process and thread containers (as C++ classes) mediator threads/processes (extensible and configurable schedulers and gatherers). The shared-memory run-time support extensively uses nonblocking lock-free (and fence-free) algorithms, the distributed run-time support employs zero-copy messaging, the GPGPUs support exploits asynchrony and SIMT optimised algorithms. | - **Building blocks** It provides the basics blocks to build (and generate via C++ header-only templates) the run-time support of core patterns. Typical objects at this level are queues (e.g. wait-free fence-free SPSC queues, bound and unbound), process and thread containers (as C++ classes) mediator threads/processes (extensible and configurable schedulers and gatherers). The shared-memory run-time support extensively uses nonblocking lock-free (and fence-free) algorithms, the distributed run-time support employs zero-copy messaging, the GPGPUs support exploits asynchrony and SIMT optimised algorithms. | ||
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| In the specific case, the only syntactic difference between OpenMP and FastFlow are that FastFlow provide programmers with C++ templates instead of compiler pragmas. It is worth to notice that despite the similar syntax, the implementation of the ''parallel_for'' and all other high-level patterns in FastFlow is quite different from OpenMP and other mainstream programming frameworks (Intel TBB, etc). FastFlow, instead of relying on a general task execution engine, generates a compile time a specific streaming network based on core patterns for each pattern. In the case of ''parallel_for'' this network is a parametric master-worker with active or passive (in memory) task scheduler (more details in the [[http://calvados.di.unipi.it/storage/paper_files/2014_ff_looppar_pdp.pdf|PDP2014 paper]]). | In the specific case, the only syntactic difference between OpenMP and FastFlow are that FastFlow provide programmers with C++ templates instead of compiler pragmas. It is worth to notice that despite the similar syntax, the implementation of the ''parallel_for'' and all other high-level patterns in FastFlow is quite different from OpenMP and other mainstream programming frameworks (Intel TBB, etc). FastFlow, instead of relying on a general task execution engine, generates a compile time a specific streaming network based on core patterns for each pattern. In the case of ''parallel_for'' this network is a parametric master-worker with active or passive (in memory) task scheduler (more details in the [[http://calvados.di.unipi.it/storage/paper_files/2014_ff_looppar_pdp.pdf|PDP2014 paper]]). | ||
| - | As in OpenMP, ''parallel_for'' comes in many variants (see user manual, <fc #FF0000>documentation in progress</fc>). Other patterns at this level, to date, are: ''parallel_reduce'', ''mdf'' (macro-data-flow), ''pool evolution'' (genetic algorithm), ''stencil''. They cover most common parallel programming paradigms in data, stream and task parallelism. Notably, FastFlow patterns are C++ class templates and can be extended by end users according to the Object-Oriented methodology. | + | As in OpenMP, ''parallel_for'' comes in many variants (see [[ffnamespace:refman|reference manual]]). Other patterns at this level, to date, are: ''parallel_reduce'', ''mdf'' (macro-data-flow), ''pool evolution'' (genetic algorithm), ''stencil''. They cover most common parallel programming paradigms in data, stream and task parallelism. Notably, FastFlow patterns are C++ class templates and can be extended by end users according to the Object-Oriented methodology. |
| Iterative execution of kernels onto GPGPUs are addressed by a single but very flexible pattern, i.e. ''stencil-reduce'', which also takes care of feeding GPGPUs with data and D2H/H2D synchronisations. More details can be found in [[http://calvados.di.unipi.it/storage/talks/2014_S4585-Marco-Aldinucci.pdf|GTC 2014 talk]]. | Iterative execution of kernels onto GPGPUs are addressed by a single but very flexible pattern, i.e. ''stencil-reduce'', which also takes care of feeding GPGPUs with data and D2H/H2D synchronisations. More details can be found in [[http://calvados.di.unipi.it/storage/talks/2014_S4585-Marco-Aldinucci.pdf|GTC 2014 talk]]. | ||
ffnamespace/architecture.txt · Last modified: 2014/09/12 19:06 by aldinuc
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