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Parallel processing divides a program’s work among multiple execution units so some tasks can run at the same time. Those units might be CPU threads sharing memory, separate processes, networked computers, or GPU threads. The right approach depends on the workload and on the cost of coordinating workers and moving data—not simply on how many cores a machine has.
What is parallel processing?
A serial program performs one operation after another. A parallel program divides suitable work into parts that can execute simultaneously, then coordinates them and combines their results when necessary. For example, a large collection of independent images could be split among workers, with each worker processing a different image.
Parallelism is not a single programming interface. It is a way of organizing computation that can use different hardware and memory models. CPU threads may share one address space; processes can keep separate memory; a distributed program may exchange messages between machines; and a GPU runs kernels over many data elements in device memory.
Concurrency and parallelism are related, but not identical
Concurrency is the organization of multiple tasks so they can make progress during overlapping periods. A system can interleave tasks on one processor without running them at the same instant. Parallelism means that multiple tasks are actually executing at the same time, typically on distinct execution units. A concurrent program may be parallel, but concurrency by itself does not guarantee simultaneous execution.
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How do parallel-processing models differ?
The major choices vary in memory access, work size, communication, and hardware. These differences influence both performance and program complexity.
| Model | Memory and work model | Typical fit | Main costs to consider |
|---|---|---|---|
| OpenMP CPU threads | Threads share memory on one host; directives divide work into parallel regions, loops, or tasks. | Loop-level or task-level work in C, C++, or Fortran on a shared-memory computer. | Synchronization, shared-data races, scheduling, and memory bandwidth. |
| Python multiprocessing | Subprocesses execute independently; data is passed or shared explicitly. | CPU-bound Python work that can be split into separate calls over multiple inputs. | Process startup, serialization, inter-process communication, and memory use. |
| CUDA GPU execution | CPU host code launches kernels on a GPU device, which runs many GPU threads over device data. | Large workloads with enough suitable parallel work to benefit from GPU execution. | Host-device data transfers, device-memory capacity, branch divergence, and synchronization. |
| Distributed processing | Processes on different machines coordinate over a network, commonly by exchanging messages. | Workloads that need more machines or can be divided across separate systems. | Network communication, coordination, failures, and managing data across machines. |
The distributed row describes the general model; the OpenMP, Python, and CUDA documentation cited below does not prescribe one distributed-system API. For the documented models, see the OpenMP specifications, Python multiprocessing documentation, and NVIDIA CUDA Programming Guide.
How does parallel processing work on a CPU?
A CPU program can divide work among threads on the same host. With shared memory, those threads can access common data, which can make communication direct—but it also means the program must control concurrent access when workers read and write the same locations.
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OpenMP: shared-memory parallelism for C, C++, and Fortran
OpenMP is a portable API for multi-platform shared-memory programming in C, C++, and Fortran. It combines compiler directives, library routines, and environment variables. A program begins with an initial thread; when it reaches a parallel region, that thread creates a team of threads to execute the region. Work-sharing constructs can assign loop iterations or other work among the team, and synchronization constructs coordinate execution.
OpenMP directives can let a compiler build a parallel version while retaining a sequential fallback when the directives are ignored. This makes it possible to express parallel work without rewriting an entire program around a separate process model. The OpenMP project lists the OpenMP 6.0 specification and softcover editions on its specifications page.
OpenMP is a practical starting point when a C, C++, or Fortran workload runs on one shared-memory host and has independent loop iterations or tasks. It does not make every loop safe to parallelize: iterations that depend on earlier iterations, or that update shared state without synchronization, can produce incorrect results.
How to approach CPU parallelization
- Find independent work. Identify iterations or tasks that can produce results without depending on unfinished work from other workers.
- Define data ownership. Decide which data each worker may read or write, and how shared updates will be protected or combined.
- Choose a parallel region or work-sharing construct. In OpenMP, use directives to express where a team executes and how work is distributed.
- Measure the whole operation. Compare end-to-end runtime and profile thread count, scheduling, synchronization, and memory bandwidth rather than assuming that adding threads will produce proportional speedup.
How does parallel processing work on a GPU?
CUDA uses a heterogeneous model: CPU code runs on the host, while GPU code runs on the device. The host prepares or transfers data, launches a GPU kernel, and coordinates with the device. A kernel launch creates many GPU threads, organized to run on the GPU’s streaming multiprocessors. CPU and GPU work can overlap, so an application may make progress on both at once.
GPU execution is most useful when a workload exposes enough similar work to keep many GPU threads busy. The apparent amount of parallel work is only part of the decision: data must reach device memory, fit there, and be processed efficiently. Transfers, synchronization, and control-flow divergence can offset the benefit of running a kernel on the GPU. Measure the full path—including data movement—not just kernel execution. See NVIDIA’s CUDA Programming Guide for the host-device model and execution details.
How can I parallelize Python code?
Python’s multiprocessing module uses subprocesses rather than relying on Python threads for CPU-bound work. Its Pool abstraction can distribute calls to a function across multiple input values. Because processes have separate execution contexts, the program must pass data to workers or arrange explicit sharing; communication and process startup add overhead.
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A process pool is a reasonable fit when a function can handle each item independently and each task does enough work to justify dispatching it to a subprocess. It is less attractive for tiny tasks or workloads that repeatedly exchange large amounts of data.
Basic process-pool pattern
from multiprocessing import Pool
def transform(value):
return value * value
if __name__ == "__main__":
values = [1, 2, 3, 4]
with Pool() as pool:
results = pool.map(transform, values)
print(results)This example maps one function over several inputs and collects the results. Keep worker functions and pool creation in an importable module, and protect the program’s entry point with the if __name__ == "__main__": guard. For production code, account for how data is serialized and transferred to workers, and handle worker errors and cleanup deliberately. See the Python multiprocessing documentation for available process and pool interfaces.
How should I choose between OpenMP, CUDA, and Python processes?
Choose based on the location of the work and the shape of the data, not on a blanket ranking of technologies. These approaches solve different problems:
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- Choose OpenMP when a C, C++, or Fortran application has suitable parallel work on one shared-memory CPU host and sharing data among threads is useful.
- Choose Python multiprocessing when CPU-bound Python tasks can be split into relatively self-contained calls and their communication overhead is acceptable.
- Choose CUDA when the workload can expose many GPU-suitable operations and the cost of getting data to and from the device is justified.
- Consider a distributed model when work or data must span machines; network communication and coordination then become part of the design.
There is no universal speedup figure that applies across these choices. The result depends on the program, hardware, task size, data movement, and coordination costs. Benchmark representative end-to-end workloads on the target system before committing to a design.
Why can parallel programs give different numeric results?
Parallel execution can change the order in which operations occur. Floating-point addition is not perfectly associative: grouping values in a different order can produce a slightly different result. A parallel reduction may combine partial sums in a different order from a serial loop, and changing the number of threads can alter that grouping.
OpenMP’s specification also makes clear that programmers are responsible for synchronizing input and output processing with OpenMP constructs or library routines. If multiple workers access shared state without suitable coordination, a data race may make results inconsistent or incorrect; synchronization helps protect correctness, though it can add overhead.
Checks for correctness and repeatability
- Identify shared variables and define which worker owns each write.
- Use appropriate synchronization for shared updates and input/output operations.
- Test with different thread counts and repeated runs, especially for reductions and race-prone code.
- Use a deterministic reduction strategy when repeatable numeric results are a requirement, and verify the acceptable error tolerance for floating-point output.
- Include transfers, process communication, and synchronization in performance measurements.
OpenMP’s execution and correctness guidance is in the OpenMP API 5.1 execution model and its specification discussion of reproducibility.
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