Computer Science
Grade 12
20 min
Parallel Computing
Parallel Computing
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1
Introduction & Learning Objectives
Learning Objectives
Differentiate between parallel and concurrent computing.
Explain the concepts of data parallelism and task parallelism with examples.
Classify parallel architectures using Flynn's Taxonomy (SIMD, MIMD).
Apply Amdahl's Law to calculate the theoretical speedup of a program.
Identify potential race conditions and deadlocks in simple parallel algorithms.
Decompose a computational problem for parallel execution on a multi-core system.
Ever wonder how a video game renders a massive, detailed world in real-time, or how a search engine sifts through billions of web pages in a fraction of a second? 🎮
This lesson explores Parallel Computing, the technique of using multiple processors simultaneously to solve a single, complex problem much faster. You...
2
Key Concepts & Vocabulary
TermDefinitionExample
Parallelism vs. ConcurrencyParallelism is about doing multiple things at the same time (simultaneous execution), requiring multiple processing units. Concurrency is about managing multiple tasks at once, which can be done by switching between them on a single processing unit.A chef with two assistants chopping vegetables at the same time is parallelism. A single chef who starts boiling water, then chops vegetables, then checks the water is concurrency.
Data ParallelismA form of parallelization where the same task is performed on different subsets of a large dataset. Each processor runs the same instruction on its piece of the data.To brighten a 1000x1000 pixel image, you could split it into four 500x500 quadrants and have four processor cores each apply the 'inc...
3
Core Syntax & Patterns
Amdahl's Law
Speedup = 1 / [ S + (P / N) ]
Used to find the maximum theoretical speedup of a program given a fixed workload. 'S' is the proportion of the program that is serial, 'P' is the proportion that is parallelizable, and 'N' is the number of processors. It shows that the serial part of a program is the ultimate bottleneck.
Flynn's Taxonomy (SIMD & MIMD)
SIMD: Single Instruction, Multiple Data. MIMD: Multiple Instruction, Multiple Data.
A high-level classification of parallel computer architectures. SIMD architectures apply the same operation to multiple data points simultaneously (common in GPUs for graphics). MIMD architectures allow different processors to execute different instructions on different data, which is the mode...
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Easy
Which statement best differentiates parallelism from concurrency, according to the tutorial's definitions?
A.Concurrency is about doing multiple things at the same time; parallelism is about managing multiple tasks.
B.Parallelism requires multiple processing units for simultaneous execution, while concurrency can be achieved on a single unit by switching tasks.
C.Parallelism is a software concept, whereas concurrency is a hardware property.
D.Concurrency always results in faster program execution than parallelism.
Easy
In Amdahl's Law, Speedup = 1 / [ S + (P / N) ], what does the variable 'S' represent?
A.The number of processors available.
B.The overall speedup achieved.
C.The proportion of the program that can be parallelized.
D.The proportion of the program that must be executed serially.
Easy
According to Flynn's Taxonomy as described in the tutorial, what does SIMD stand for?
A.Sequential Instruction, Multiple Devices
B.Single Instruction, Many Devices
C.Single Instruction, Multiple Data
D.Simple Instruction, Multiple Data
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