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ffnamespace:architecture [2014/08/04 18:28]
aldinuc [High-level Patterns]
ffnamespace:architecture [2014/08/14 13:36]
aldinuc [Core 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'',​ ''​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 are: <fc #​FF0000>​documentation in progress</​fc>​. 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]]. ​
 +
 ==== Core Patterns ==== ==== Core Patterns ====
 +At its foundations FastFlow implements a (mid/​low-level) concurrent programming model, which extends C++ language. From the orchestration viewpoint, the process model to be employed is a CSP/Actor hybrid model where processes (so-called ''​ff_node''​s) are named and the data paths between processes are clearly identified. The abstract units of communication and synchronisation are known as ''​channels''​ and represent a stream of data dependency between two processes. A ''​ff_node''​ is C++ class, after construction it enters in a infinite loop
 +that 1) gets a task from input channel (i.e. a pointer); 2) execute business code on the task; 3) put a task the output channel (i.e. a pointer). Representing communication and synchronisation as a channel ensures that synchronisation is tied to communication and allows layers of abstraction at higher levels to compose parallel programs where synchronisation is implicit. ​
  
- +At the core patterns level, patterns to build a graph of ''​ff_node''​s are defined. Since the graph of ''​ff_node''​s is a streaming network, any FastFlow graph is built using two streaming patterns (''​farm''​ and ''​pipeline''​) and one pattern-modifier (''​loopback'',​ to build cyclic networks). These patterns can be arbitrarily nested to build large and complex graphs. However, not all graphs can be build. This enforce the correctness (by-construction) of all streaming networks that can be generated. In particular, they are deadlock-free and data-race free.
-=== Accelerator mode ===+
  
 === Nonblocking and Blocking behaviour === === Nonblocking and Blocking behaviour ===
 +
 +FastFlow run-time is designed to exhibit a nonblocking behaviour (by way of lock-free and wait-free algorithms, at least in the synchronisation critical paths). This design choice mainly targets efficiency for very fine grain parallelism.  ​
  
 === Deadlock avoidance === === Deadlock avoidance ===
 +
 +=== Accelerator mode ===
 +
 +The offloading feature is concerned with abstracting over auxiliary hardware or software accelerators (e.g. GPGPUs or set-of-cores). Offloading allows the model to be heterogeneous over the hardware in that code that is to be executed on a CPU which makes use of this layer may be partially offloaded onto accelerators.
 +
 +At the level of core patterns, all pattern composition can work in //​accelerator mode//. A software accelerator can be feed with asynchronous offloading requests from the main thread (or any other thread), which basically generate a input stream for the accelerator. The primary aim of offloading is to provide the high-level pattern (or application programmer) programmer with an easy and semi-automatic path to introducing parallelism into a C/C++ sequential code by moving or copying parts of the original code into the body of C++ methods, which will be executed in parallel according to a FastFlow pattern (or pattern composition). Results computed from an accelerator can be collected either synchronously or asynchronously.
 +
 +A FastFlow accelerator provides the programmer with one (untyped) streaming input channel and one (untyped) streaming output channel that can be dynamically //created// (and //​destroyed//​) from a C++ code (either sequential or multi-threaded) as a C++ object. Thanks to the underlying shared memory architecture,​ messages flowing into these channels may carry both values and pointers to data structures. ​
 +
 +An accelerator,​ which is a collection of threads, ​ has a global lifecycle with
 +two stable states: \emph{running} and
 +\emph{frozen},​ plus several transient states. The running state happens
 +when all threads are logically able to run (i.e. they are ready or
 +running at the O.S. level). ​ The frozen state happens when all threads
 +are suspended (at the O.S. level). Transitions from these two
 +states involve calls to the underlying threading library (and to the O.S.).
 +
 +Once created, an accelerator can be run, making it capable of accepting tasks on the input channel. When running, the threads belonging to an accelerator might fall into an //active waiting// state. These state transitions exhibit a very low overhead and do not involve the O.S.
 +threads not belonging to the accelerator could //wait// for an accelerator,​ i.e. suspend until the accelerator completes its input tasks (receives the //​End-of-Stream//,​ unique is propagated in transient states of the lifecycle to all threads) ​ and then put it in the frozen state. At creation time, the accelerator is configured and its threads are bound into one or more  cores. Since the FastFlow run-time is implemented via non-blocking threads, they will, if not frozen, fully load the cores in which they are placed, no matter whether they are actually processing something or not. Because of this, the accelerator is usually configured to use "​spare"​ cores
 +(although over-provisioning could be forced). ​ If necessary, output tasks could be popped from the accelerator output channel.
 +
 +More details on FastFlow accelerator technology can be found in [ADK11]. ​
 +
  
  
 ==== Building Blocks ==== ==== Building Blocks ====
-At its foundations FastFlow realises a (low-level) concurrent programming model, which extends C++ language. From the orchestration viewpoint, the process model to be employed is a CSP/Actor hybrid model where processes (so-called ''​ff_node''​s) are named and the data paths between processes are clearly identified. The abstract units of communication and synchronisation are known as ''​channels''​ and represent a data dependency between two processes. Representing communication and synchronisation as a channel ensures that synchronisation is tied to communication and allows layers of abstraction at higher levels to compose parallel programs where synchronisation is implicit. The offloading feature is concerned with abstracting over auxiliary hardware or software accelerators (e.g. GPGPUs or set-of-cores). Offloading allows the model to be heterogeneous over the hardware in that code that is to be executed on a CPU which makes use of this layer may be partially offloaded onto accelerators.+ 
  
 At this level, FastFlow programming model can be thought as a hybrid shared-memory/​message-passing model. A process (''​ff_node''​) is sequential, a channel models a true data dependency between processes. Processes typically stream data items (they are not tasks) onto channels, they can be either references (e.g. pointers in the shared-memory) or messages with a payload (e.g. in a distributed platform). In both cases, the data item acts as synchronisation token. In general, no further synchronisation primitives are needed (e.g. locks, semaphores) even thought their usage is not forbidden (they are simply useless and a source of additional overhead). Overall, at this level, FastFlow building blocks make it possible to realise arbitrary streaming networks over lock-less channels. ​ At this level, FastFlow programming model can be thought as a hybrid shared-memory/​message-passing model. A process (''​ff_node''​) is sequential, a channel models a true data dependency between processes. Processes typically stream data items (they are not tasks) onto channels, they can be either references (e.g. pointers in the shared-memory) or messages with a payload (e.g. in a distributed platform). In both cases, the data item acts as synchronisation token. In general, no further synchronisation primitives are needed (e.g. locks, semaphores) even thought their usage is not forbidden (they are simply useless and a source of additional overhead). Overall, at this level, FastFlow building blocks make it possible to realise arbitrary streaming networks over lock-less channels. ​
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 === Distributed platforms === === Distributed platforms ===
  
- +Documentation ​in progress
- +
- +
-<note warning>​OLD page - work in progress</​note>​ +
- +
-===== FastFlow tiers details =====+
  
 ==== Hardware ==== ==== Hardware ====
Line 122: Line 145:
  
  
 +===== Rationale =====
  
- +Parallelism ​exploitation patterns (a.k.a. {{http://​en.wikipedia.org/​wiki/​Algorithmic_skeleton|skeletons}}) are usually ​categorised ​in three main classes: **Task**, **Data**, and **Stream** Parallelism. ​
-==== Core Patterns ==== +
- +
-<note tip>To be detailed</​note>​ +
- +
-==== High-level Patterns ==== +
- +
-<note important>​To be updated</​note>​ +
- +
-The next layer up, i.e. //Core Patterns//, ​ provides a programming framework based on parallelism ​exploitation patterns (a.k.a. {{http://​en.wikipedia.org/​wiki/​Algorithmic_skeleton|skeletons}}). They are usually ​categorized ​in three main classes: **Task**, **Data**, and **Stream** Parallelism.  +
-FastFlow specifically focuses on Stream Parallelism,​ and in particular provides: +
-''​farm'',​ ''​farm-with-feedback''​ (i.e. Divide\&​Conquer),​ ''​pipeline'',​ and their arbitrary nesting and composition. The set of skeletons provided by FastFlow could be further extended by building new C++ templates.+
  
   * **Stream Parallelism** can be used when there exists a partial or total order in a computation. By processing data elements in order, local state may be maintained in each filter. The set of skeletons provided by FastFlow could be further extended by building new C++ templates on top  of the Fastflow low-level programming layer.   * **Stream Parallelism** can be used when there exists a partial or total order in a computation. By processing data elements in order, local state may be maintained in each filter. The set of skeletons provided by FastFlow could be further extended by building new C++ templates on top  of the Fastflow low-level programming layer.
Line 140: Line 153:
   * **Data Parallelism** is a method for parallelizing a single task by processing independent data elements of this task in parallel. The flexibility of the technique relies upon stateless processing routines implying that the data elements must be fully independent. Data Parallelism also supports Loop-level Parallelism where successive iterations of a loop working on independent or read-only data are parallelized in different flows-of-control and concurrently executed.   * **Data Parallelism** is a method for parallelizing a single task by processing independent data elements of this task in parallel. The flexibility of the technique relies upon stateless processing routines implying that the data elements must be fully independent. Data Parallelism also supports Loop-level Parallelism where successive iterations of a loop working on independent or read-only data are parallelized in different flows-of-control and concurrently executed.
  
 +FastFlow is designed to support all of them over a stream parallel programming model (provided by core patterns tier). The set of patterns provided by FastFlow could be further extended by building new C++ templates.
  
 While many of the  programming frameworks for multi-core offer Data and Task Parallel skeletons, only few of them offer Stream Parallel skeletons (such as TBB's //​pipeline//​). None of them  offers the //farm// skeleton, which exploits ​ functional replication of a set of //workers// and  abstracts out the parallel filtering of successive //​independent//​ items of the stream under the control of a scheduler, as a first-class concept. While many of the  programming frameworks for multi-core offer Data and Task Parallel skeletons, only few of them offer Stream Parallel skeletons (such as TBB's //​pipeline//​). None of them  offers the //farm// skeleton, which exploits ​ functional replication of a set of //workers// and  abstracts out the parallel filtering of successive //​independent//​ items of the stream under the control of a scheduler, as a first-class concept.
- 
-===== Problem Solving Environments (PSEs) ===== 
- 
-==== Fastflow accelerator ​ ==== 
-A //FastFlow accelerator//​ is a software device wrapping a high-level FastFlow program, i.e. a skeleton or a composition of skeletons, and providing the application programmer with a functional //​self-offloading//​ feature, since the offload happens on the same hardware device, i.e. CPU cores. The primary aim of self-offloading is to provide the programmer with an easy and semi-automatic path to introducing parallelism into a C/C++ sequential code by moving or copying parts of the original code into the body of C++ methods, which will be executed in parallel according to a FastFlow skeleton (or skeleton composition). This requires limited programming effort and it may speed up  the original code by exploiting unused cores. 
- 
-A FastFlow accelerator provides the programmer with one (untyped) streaming input channel and one (untyped) streaming output channel that can be dynamically //created// (and //​destroyed//​) from a C++ code (either sequential or multi-threaded) as a C++ object. Thanks to the underlying shared memory architecture,​ messages flowing into these channels may carry both values and pointers to data structures. ​ 
- 
-An accelerator,​ which is a collection of threads, ​ has a global lifecycle with 
-two stable states: \emph{running} and 
-\emph{frozen},​ plus several transient states. The running state happens 
-when all threads are logically able to run (i.e. they are ready or 
-running at the O.S. level). ​ The frozen state happens when all threads 
-are suspended (at the O.S. level). Transitions from these two 
-states involve calls to the underlying threading library (and to the O.S.). 
- 
-Once created, an accelerator can be run, making it capable of accepting tasks on the input channel. When running, the threads belonging to an accelerator might fall into an //active waiting// state. These state transitions exhibit a very low overhead and do not involve the O.S. 
-threads not belonging to the accelerator could //wait// for an accelerator,​ i.e. suspend until the accelerator completes its input tasks (receives the //​End-of-Stream//,​ unique is propagated in transient states of the lifecycle to all threads) ​ and then put it in the frozen state. At creation time, the accelerator is configured and its threads are bound into one or more  cores. Since the FastFlow run-time is implemented via non-blocking threads, they will, if not frozen, fully load the cores in which they are placed, no matter whether they are actually processing something or not. Because of this, the accelerator is usually configured to use "​spare"​ cores 
-(although over-provisioning could be forced). ​ If necessary, output tasks could be popped from the accelerator output channel. 
- 
-More details on //HOWTO// use a FastFlow accelerator can be found in {{http://​www.di.unipi.it/​~aldinuc/​paper_files/​TR-10-03.pdf|TR-10-03}},​ and in several examples within the {{http://​sourceforge.net/​projects/​mc-fastflow/​|FastFlow tarball available from sourceforge}. 
- 
- 
  
  
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 [AMT09] M. Aldinucci, M. Meneghin, and M. Torquati. ​ Efficient Smith-Waterman on multi-core with fastflow. In Proc. of Intl. Euromicro PDP 2010: Parallel Distributed and network-based Processing, Pisa, Italy, Feb. 2010. IEEE. To appear. [[ffnamespace:​about|(Paper Draft)]] [AMT09] M. Aldinucci, M. Meneghin, and M. Torquati. ​ Efficient Smith-Waterman on multi-core with fastflow. In Proc. of Intl. Euromicro PDP 2010: Parallel Distributed and network-based Processing, Pisa, Italy, Feb. 2010. IEEE. To appear. [[ffnamespace:​about|(Paper Draft)]]
  
-[ADK12] M. Aldinucci, M. Danelutto, P. Kilpatrick, M. Meneghin, and M. Torquati. ​An efficient unbounded lock-free queue for multi-core systems. In Proc. of 18th Intl. Euro-Par ​2012 Parallel Processing, volume ​7484 of LNCS, pages 662673Rhodes IslandGreeceaug 2012. Springer.+[ADK11] M. Aldinucci, M. Danelutto, P. Kilpatrick, M. Meneghin, and M. Torquati. ​Accelerating code on multi- ​cores with fastflow. In Proc. of 17th Intl. Euro-Par ​2011 Parallel Processing, volume ​6853 of LNCS, pages 170181BordeauxFranceAug. 2011. Springer.
  
 +[ADK12] M. Aldinucci, M. Danelutto, P. Kilpatrick, M. Meneghin, and M. Torquati. An efficient unbounded lock-free queue for multi-core systems. In Proc. of 18th Intl. Euro-Par 2012 Parallel Processing, volume 7484 of LNCS, pages 662–673, Rhodes Island, Greece, aug 2012. Springer.
ffnamespace/architecture.txt · Last modified: 2014/09/12 19:06 by aldinuc