Parallel Computing in Python: multiprocessing
Parallel Computing in Python:
multiprocessing
Konrad HINSEN Centre de Biophysique Mol?culaire (Orl?ans)
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
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