[PDF] Parallel Computing in Python: multiprocessing

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Mémo Python Lycée int 783 0 -192 boucle sur dict/set = boucle sur séquence des clés en liste pour voir les valeurs, par exemple:

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Parallel Computing in Python: multiprocessing

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Parallel Computing

in Python: multiprocessing

Konrad HINSEN

Centre de Biophysique Moléculaire (Orléans)

and

Synchrotron Soleil (St Aubin)

Parallel computing:

Theory

Parallel computers

Multiprocessor/multicore:

several processors work on data stored in shared memory

Cluster:

several processor/memory units work together by exchanging data over a network

Co-processor:

a general-purpose processor delegates specific tasks to a special-purpose processor (GPU, FPGA, ...)

Other:

- Cluster of multicore nodes with GPUs - NUMA (non-uniform memory access) architectures

Almost all computers made today are parallel!

Parallelism vs. concurrency

Parallelism:

use multiple processors to make a computation faster.

Concurrency:

permit multiple tasks to proceed without waiting for each other. Different goals that share implementation aspects. Scientific computing cares more about parallelism.

Concurrency is rarely needed.

Parallel Programming

Parallel Programming

Decomposition of the complete task into independent subtasks and the data flow between them.

Parallel Programming

Decomposition of the complete task into independent subtasks and the data flow between them.

Distribution of the subtasks over the processors

minimizing the total execution time.

Parallel Programming

Decomposition of the complete task into independent subtasks and the data flow between them.

Distribution of the subtasks over the processors

minimizing the total execution time. For clusters: distribution of the data over the nodes minimizing the communication time.

Parallel Programming

Decomposition of the complete task into independent subtasks and the data flow between them.

Distribution of the subtasks over the processors

minimizing the total execution time. For clusters: distribution of the data over the nodes minimizing the communication time. For multiprocessors: optimization of the memory access patterns minimizing waiting times.

Parallel Programming

Decomposition of the complete task into independent subtasks and the data flow between them.

Distribution of the subtasks over the processors

minimizing the total execution time. For clusters: distribution of the data over the nodes minimizing the communication time. For multiprocessors: optimization of the memory access patterns minimizing waiting times.

Synchronization of the individual processes.

Difficulties

Verifying that subtasks are indeed independent.

Making synchronization patterns deadlock-free.

Clusters: verifying communication patterns.

Correctness:

Difficulties

Verifying that subtasks are indeed independent.

Making synchronization patterns deadlock-free.

Clusters: verifying communication patterns.

Attributing equal workloads to all processors.

Taking into account computation and communication.

Optimizing for a specific parallel computer.

Efficiency:Correctness:

Synchronization issues

Deadlock:

- Two processes are waiting for each other to finish. - Usually caused by locks or by blocking communication.

Race condition:

- Two or more processes modify a shared resource (variable, file, ...) - Result depends on which process comes first. - Can be avoided using locks, but... - ... handling locks is very difficult and mistakes often cause deadlocks.

Parallel Programming Models

High-level: express parallel algorithms rather than parallel computing hardware. Deterministic: a program should have a unique result that depends only on its inputs and not on the number of processors or on random factors (machine load etc.). Universal: efficiently applicable to clusters, multiprocessors, etc.

General: applicable to all parallel algorithms.

Modular: permit the construction of composable libraries. Simple: both the meaning and the cost (time and memory) of each statement or construct should be clear. Many parallel programming models have been proposed, more are currently being developed. This is a very active field of research.

The ideal programming model should be

Parallel Programming Models

Message-passing: a low-level non-deterministic general model for programming clusters. Used by the very popularquotesdbs_dbs3.pdfusesText_6