Skip to main content

Intro to Parallel Programming

📚 Recommended Books​

📢 Amazon Affiliate Tip:If you purchase through these links, we may earn a small commission at no extra cost to you.
👉 Browse More Parallel Programming Books on Amazon

⚡ Recommended Books on Parallel & GPU Programming

📗 CUDA Programming: A Developer's Guide to Parallel Computing with GPUs – Shane Cook

Author: Shane Cook

An advanced and practical guide for developers working with NVIDIA CUDA to harness GPU power in high-performance computing applications.

🔹 What to Expect:

  • Covers advanced CUDA concepts like streams and memory optimization
  • Includes debugging, profiling, and optimization techniques
  • Real-world use cases and design patterns
  • Ideal for professionals working in HPC or deep learning
👉 Buy Now on Amazon
📗 GPU Parallel Program Development Using CUDA – Tolga Soyata

Author: Tolga Soyata

This book introduces parallel programming concepts using CUDA with a focus on real-world applications and performance optimization.

🔹 What to Expect:

  • Step-by-step CUDA project development workflow
  • Topics like memory hierarchy, warp scheduling, and coalescing
  • Useful for students and professionals diving into GPU programming
  • Includes lab-style exercises and examples
👉 Buy Now on Amazon
📗 Programming Massively Parallel Processors – David B. Kirk & Wen-mei W. Hwu

Author: David B. Kirk & Wen-mei W. Hwu

A comprehensive guide to GPU computing and CUDA programming, this book covers parallel computation fundamentals, optimization strategies, and practical CUDA code examples.

🔹 What to Expect:

  • Introduction to GPU architecture and CUDA programming
  • Covers parallel algorithm design and performance analysis
  • Practical examples for real-world problems
  • Used widely in academic courses and industry
👉 Buy Now on Amazon
📗 Parallel Programming in C with MPI and OpenMP – Michael J. Quinn

Author: Michael J. Quinn

Focused on writing parallel programs in C using MPI and OpenMP, this book is ideal for students and developers looking to build high-performance applications.

🔹 What to Expect:

  • Fundamentals of message passing and shared-memory models
  • Hands-on C code with MPI and OpenMP
  • Topics include load balancing, synchronization, and scalability
  • Great for academic courses and competitive coding
👉 Buy Now on Amazon
📗 CUDA by Example: An Introduction to General-Purpose GPU Programming – Jason Sanders & Edward Kandrot

Author: Jason Sanders & Edward Kandrot

This book introduces CUDA programming through practical examples and intuitive explanations, making it easy for newcomers to dive into GPU computing.

🔹 What to Expect:

  • Step-by-step CUDA code explanations
  • Focus on accelerating real applications with GPU
  • Easy transition from serial to parallel thinking
  • Ideal for beginners in GPU programming
👉 Buy Now on Amazon
📗 Introduction to Parallel Computing – Ananth Grama, Anshul Gupta, George Karypis & Vipin Kumar

Author: Ananth Grama, Anshul Gupta, George Karypis & Vipin Kumar

An in-depth resource on parallel algorithm design, performance modeling, and scalability across different hardware platforms.

🔹 What to Expect:

  • Detailed discussion on parallel architectures
  • Task and data decomposition strategies
  • Modeling, analysis, and scalability considerations
  • Academic and research-oriented content
👉 Buy Now on Amazon
📗 High Performance Python – Micha Gorelick & Ian Ozsvald

Author: Micha Gorelick & Ian Ozsvald

A practical guide to optimizing Python programs using parallelism, concurrency, and performance tools.

🔹 What to Expect:

  • Techniques for parallel processing and concurrency in Python
  • Cython, NumPy, and multi-core optimization examples
  • Best practices for memory and CPU usage
  • Useful for data scientists and engineers
👉 Buy Now on Amazon

Getting Started

tip

Tips Parallel programming improves efficiency by utilizing multiple computing units to perform tasks simultaneously. This concept can be better understood through a real-life analogy: grocery shopping.

Understanding Parallel Programming With Scenarios​

Imagine Abhinav and Priya go shopping. Their goal is to buy all the items on their list efficiently. Each scenario represents a different computing execution model.

Single-Core, Single-Thread Execution (Basic CPU Execution)​

Abhinav alone does all the work step by step, similar to how a basic CPU executes one instruction at a time.

Steps:

  1. Reads the grocery list.
  2. Searches for the first item in the store.
  3. Picks the item and places it in the cart.
  4. Moves to the next item on the list.
  5. Repeats until all items are collected.

Problem: Only one instruction is executed at a time, making the process slow.

Analogy in Computing: A single-core CPU executes one task at a time, waiting for each step to complete before starting the next.

Key Takeaways​

Execution ModelExampleCoresParallelismBest Use Case
Single-CoreAbhinav shopping alone1NoSimple, low-power tasks
Dual-Core (Pipeline)Abhinav reads, Priya searches2LimitedFaster execution, but not full parallelism
No Thread ParallelismTask dependency limits speed2NoMulti-core system without efficiency
True Dual-CoreBoth work independently2YesFast multitasking, independent execution
GPU ExecutionMultiple workers in a store1000+HighHighly parallelizable tasks like AI, graphics

Table of Contents

1. Introduction to Parallel Programming​

2. Our First Serial Program (Matrix Multiplication)​

3. How We Can Make It Parallel?​

4. Common Questions & Challenges in Parallel Computing​

5. References​

What is Core​

A CPU core is like an individual worker in a team, capable of executing tasks independently.
The more cores a CPU has, the more tasks it can handle simultaneously.

A core is the fundamental processing unit of a CPU. Each core can execute its own set of instructions, allowing for multitasking and improved performance in multi-threaded applications. Modern CPUs have multiple cores to enhance speed and efficiency.

A CPU core is like an individual worker in a team, capable of executing tasks independently.
The more cores a CPU has, the more tasks it can handle simultaneously.

A core is the fundamental processing unit of a CPU. Each core can execute its own set of instructions, allowing for multitasking and improved performance in multi-threaded applications. Modern CPUs have multiple cores to enhance speed and efficiency.

Understanding CPU Cores with a Shopping Analogy​

To understand how CPU cores work, let's consider a real-life analogy of Abhinav and Priya shopping for groceries.

🛒 How Different CPU Configurations Work?​

🛒 Single-Core Execution (One Person Shopping)
  • Imagine Abhinav is shopping alone.
  • He reads the grocery list, finds an item, places it in the cart, and repeats this process until everything is collected.
  • 🛑 Problem: Since Abhinav is doing everything step by step, the process is slow.

💻 Computing Analogy: A single-core CPU executes one instruction at a time. It must complete one task before moving to the next.

📝 Step-by-Step Process​

1️⃣ Read first item from the list.
2️⃣ Walk to the location, pick up the item.
3️⃣ Read the next item, walk to its location, pick it up.
🔄 Repeat until all items are collected.

🛍️ Two-Core CPU Without True Parallelism (Instruction Pipelining)
  • Now, Abhinav and Priya shop together, but with a dependency.
  • Abhinav reads the next item on the list while Priya searches for the previous one.
  • Priya can only start after Abhinav reads the item, introducing a slight delay.

📌 Instruction Pipelining Diagram​

🧑 Abhinav (Reads Items) ---> 🧑 Priya (Searches Items)
🧑 Reads item 1 → 🧑 Starts searching item 1
🧑 Reads item 2 → 🧑 Starts searching item 2
🧑 Reads item 3 → 🧑 Starts searching item 3
🔄 True Parallelism with Two Cores & Two Threads
  • This time, Abhinav and Priya each have their own list and shop independently.
  • They collect items at the same time, doubling the speed.
  • ✅ No dependency or delay—both work efficiently in parallel.

📌 True Parallelism Diagram​

🧑 Abhinav (Shops Independently) → Places Item 1
🧑 Priya (Shops Independently) → Places Item 2
🧑 Places Item 3 → 🧑 Places Item 4

🛒 How Do Both Place Items?​

When both Abhinav and Priya shop independently, they place items in the cart simultaneously. This mirrors how multi-core CPUs process instructions concurrently. Each core executes its own set of instructions without waiting for the other, maximizing efficiency and reducing completion time.

✅ Example in Computing:

  • If Abhinav represents Core 1 and Priya represents Core 2, then both cores work in parallel on different tasks.
  • When Abhinav places an item, Priya can simultaneously place another item.
  • This reduces the overall shopping time, just like a multi-core CPU improves processing speed by handling multiple tasks at once.

1.2 Cores vs Threads​

Understanding the difference between CPU cores and threads is essential in grasping how modern processors handle multiple tasks efficiently.

  • A CPU core is a physical processing unit that independently executes instructions.
  • A thread is a logical unit that allows a core to manage multiple tasks simultaneously, improving efficiency but not truly doubling performance.

To make this concept intuitive, let’s explore it using the Abhinav & Priya Shopping Analogy! 🛍️

***🧠 Cores vs Threads ***​

🛒 Scenario 1: Two Independent Shoppers (Cores)​

🧑💻 Cores: Two Shoppers with Separate Lists
  • Imagine Abhinav and Priya enter a shopping mall, each with their own shopping list.
  • They split up and shop independently, each picking items for their respective lists at the same time.
  • Since they don’t interfere with each other, they finish shopping twice as fast.

💻 Computing Analogy:

  • Each CPU core in a multi-core processor functions like an independent shopper, capable of executing its own set of instructions simultaneously.
  • More cores mean true parallel execution, leading to better performance for multi-threaded tasks.

✅ True parallelism: Faster, independent execution!

🔄 Scenario 2: A Single Shopper with a Helper (Threads)​

🔄 Threads: One Shopper with an Assistant
  • Now, imagine Abhinav goes shopping alone, but he brings along his brother Aditya to help.
  • They have only one shopping cart. While Aditya can assist by looking at the list and suggesting what’s next, only one person can pick items at a time.
  • This speeds up the process compared to a single shopper, but it's not as fast as two independent shoppers.

💻 Computing Analogy:

  • A thread is like a helper within a single core. It improves multitasking (e.g., hyper-threading), but still shares the same physical resources.
  • Threads allow the CPU to work more efficiently but don’t double the speed like multiple cores do.

✅ More efficient than a single worker, but still resource-limited!

📊 Cores vs Threads: Key Differences​

FeatureCores 🧑💻 (Independent Shoppers)Threads 🔄 (Helper System)
DefinitionPhysical CPU unitVirtual processing unit within a core
ExecutionTrue parallel executionTime-sliced execution (shared resources)
PerformanceHigher performance, truly independentImproves multitasking but doesn't double speed
EfficiencyMore cores = faster executionMore threads = better resource utilization
ExampleTwo people shopping separatelyOne person shopping with a helper

🎯 Summary: Which Matters More?​

  • More cores = Better performance for multi-threaded applications (e.g., gaming, video editing, rendering, AI workloads).
  • More threads = Better multitasking, but performance gains depend on software optimization (e.g., hyper-threading in Intel CPUs).
  • For most users, a balance of high-core-count and multi-threading provides the best experience!

📌 Next time you buy a CPU, check both the number of cores and whether it supports multi-threading! 🚀

1.3 Does More Cores Always Mean More Parallelism?​

💡 The Myth of More Cores = More Speed​

It’s a common belief that adding more cores to a CPU automatically results in better parallelism and faster performance. While this is partially true, the reality is more nuanced.

🔍 Understanding the Limits of Parallelism​

  • Not All Workloads Are Parallelizable: Some tasks can’t be split across multiple cores effectively. For example, a single-threaded application won’t benefit from multiple cores.
  • Diminishing Returns: Adding more cores can improve performance only up to a certain point. If a program isn’t optimized for multi-threading, extra cores remain underutilized.
  • Synchronization Overhead: More cores require better coordination. If tasks need to share data frequently, the overhead of synchronization can reduce efficiency.
  • Memory and Bandwidth Constraints: More cores mean higher demand for memory access. If the memory bandwidth doesn’t scale accordingly, cores may sit idle waiting for data.

📊 When More Cores Help (and When They Don’t)​

ScenarioMore Cores Help?Why?
Multi-threaded applications✅ YesTasks can be split across cores.
Single-threaded programs❌ NoThey run on only one core.
Gaming⚠️ SometimesDepends on game optimization.
Video rendering & AI workloads✅ YesHighly parallelizable tasks.
General web browsing❌ NoNot CPU-intensive.

🚀 Key TakeAway: More Cores ≠ Always Faster​

  • More cores can improve performance, but only when software is designed to utilize them efficiently.
  • A balance between core count, clock speed, and software optimization is crucial for real-world performance gains.
tip

Next time you choose a CPU, consider your workload instead of just the core count!

1.4 More Threads or More Cores for Better Parallelism?​

🧩 The Core vs. Thread Dilemma​

When it comes to boosting parallelism, both cores and threads play vital roles—but they work differently.

  • Cores are the physical units of computation.
  • Threads are logical or virtual divisions of those cores.

So which one gives better performance for parallel tasks? Let's break it down.

🔬 Threads: Lighter but Limited​

  • Threads are designed to improve utilization of a core by allowing it to work on multiple tasks via time-slicing.
  • Technologies like Intel Hyper-Threading and SMT (Simultaneous Multi-Threading) allow one core to manage more than one thread.
  • But remember: threads share the same core resources (cache, execution units), so performance gains are not linear.

🧠 Best for: Light-weight, high-concurrency tasks like I/O handling, web servers, or UI responsiveness.

💪 Cores: Heavier but True Parallelism​

  • Cores can handle separate instructions truly in parallel.
  • More cores mean you can run more threads independently, without them fighting for shared resources.

🧠 Best for: Heavy, compute-bound workloads like rendering, simulations, compilation, and gaming (if optimized).

⚖️ The Balanced Perspective​

FeatureMore Cores 💻More Threads 🔁
TypePhysicalLogical
Resource SharingNoYes
Speed Boost✅ Higher⚠️ Moderate
Best Use CaseCPU-intensiveI/O-intensive

🚀 Verdict: Use Both—But Know Your Workload​

  • More cores give you raw parallel power.
  • More threads let you keep your cores busy and improve responsiveness.
  • The best performance comes from a balanced architecture paired with software that knows how to use it.
tip

🎯 Know your task. Choose wisely. Optimize accordingly.

2. Our First Serial Program (Matrix Multiplication)​

2.1 How Does Matrix Multiplication Work?​

Matrix multiplication is the process of taking two matrices, A and B, and producing a third matrix C, where each element C[i][j] is computed as the dot product of the i-th row of A and the j-th column of B.

Formula:​

Let A be an m × n matrix and B be an n × p matrix. Then C = A × B is:

C[i][j]=∑k=0n−1A[i][k]×B[k][j]C[i][j] = \sum_{k=0}^{n-1} A[i][k] \times B[k][j]
  • Matrix A: dimensions m × n
  • Matrix B: dimensions n × p
  • Result Matrix C: dimensions m × p

2.2 Matrix Multiplication Code in Different Languages​

#include <iostream>
#include <vector>
using namespace std;

int main() {
    int m = 2, n = 2, p = 2;
    vector<vector<int>> A = {{1, 2}, {3, 4}};
    vector<vector<int>> B = {{5, 6}, {7, 8}};
    vector<vector<int>> C(m, vector<int>(p, 0));

    for (int i = 0; i < m; ++i)
        for (int j = 0; j < p; ++j)
            for (int k = 0; k < n; ++k)
                C[i][j] += A[i][k] * B[k][j];

    for (auto row : C) {
        for (auto val : row) cout << val << " ";
        cout << endl;
    }
    return 0;
}

2.3 Performance Analysis (C++ Chosen for Benchmarking)​

To benchmark matrix multiplication in C++, we avoid using chrono and instead demonstrate platform-specific timing tools.

Use the time command on Linux or WSL:

clang++ matrix.cpp -o matrix
/usr/bin/time -f "%e seconds" ./matrix

This will show real execution time in seconds.

This reports real, user, and sys time.

note

This experiment is done on mac os

Example Output:

time ./a.out
19 22
43 50
./a.out  0.00s user 0.00s system 1% cpu 0.353 total

Explanation:

  • 19 22 and 43 50: This is the output of your matrix multiplication program (i.e., the result matrix).
  • 0.00s user: The amount of time the CPU spent in user mode (your program's code).
  • 0.00s system: Time spent by the system (kernel) on behalf of your program.
  • 1% cpu: Percentage of CPU used during the total time.
  • 0.353 total: The real (wall-clock) time it took to run the program from start to finish.
tip

🧠 Insight:
For large-scale matrix operations, use high-performance libraries such as:

How can we Make it Parallel​

3.1 Understanding Dependencies in Parallel Computing (RAW, WAR, RAR, WAW)​

When parallelizing code, it's essential to understand and manage data dependencies to maintain correctness and avoid race conditions. The four fundamental types of dependencies are: When parallelizing code, it's essential to understand and manage data dependencies to maintain correctness and avoid race conditions. These dependencies define how data is used and shared between different operations or threads, and failing to handle them properly can lead to incorrect results or difficult-to-debug behavior.

The Four Types of Data Dependencies

The Four Types of Data Dependencies and Why They Matter:​

  1. True Dependency (Read After Write - RAW)
    Occurs when a statement depends on the result of a previous statement.
    Why it matters: If the dependent instruction is executed before the data is written, it may read an incorrect or stale value.

  2. Anti-Dependency (Write After Read - WAR)
    Happens when a statement writes to a location that a previous statement is reading from.
    Why it matters: If the write happens before the read completes, it may overwrite the data too soon.

  3. Output Dependency (Write After Write - WAW)
    Arises when two statements write to the same location.
    Why it matters: The final result can be unpredictable if the order of writes changes.

  4. Input Dependency (Read After Read - RAR)
    Occurs when two statements read from the same location.
    Why it matters: This is generally safe, but understanding it helps optimize memory access patterns for performance.

These dependencies determine the scheduling and synchronization required to safely parallelize sections of code.

RAW (Read After Write) – True Dependency​

Occurs when an instruction needs to read a value that must be written by a previous instruction.

Example in Matrix Multiplication:

C[i][j] = A[i][k] * B[k][j];  // Write to C[i][j]
sum = C[i][j] + temp;         // Read from C[i][j]

Here, sum depends on the value written to C[i][j] earlier. This must be preserved when parallelizing.

note

Understanding and handling these dependencies is crucial for writing correct and efficient parallel programs

3.2 Matrix Multiplication Dependencies Explained​

In matrix multiplication, we are given:

  • Matrix A of size M x K
  • Matrix B of size K x N
  • Result matrix C of size M x N

Each element in the result matrix C[i][j] is computed as:

C[i][j] = A[i][0] * B[0][j] + A[i][1] * B[1][j] + ... + A[i][K-1] * B[K-1][j]

Or more generally:

C[i][j] = ∑ (A[i][k] * B[k][j]) for k = 0 to K-1

🔍 Dependency Analysis​

To compute C[i][j], we need:

  • The i-th row of matrix A: A[i][0] ... A[i][K-1]
  • The j-th column of matrix B: B[0][j] ... B[K-1][j]

Therefore:

  • C[i][j] is only dependent on one row from A and one column from B.
  • It does not depend on any other element of the result matrix C.

✅ Independence of Computations​

Because each C[i][j] uses only:

  • A specific row from A
  • A specific column from B

And no other element of C is involved, this means:

  • There are no read-write conflicts between different elements of C
  • Each computation of C[i][j] is independent of others

This independence allows us to compute all C[i][j] values in parallel.

Before Parallelism
note

📝 Before Parallelism​

Before assuming parallelism is ideal, always:

  • Verify that the environment supports efficient parallel operations (e.g., hardware capabilities)
  • Check if memory bandwidth and synchronization overhead are within acceptable limits
  • Ensure that there are no side effects (e.g., shared memory writes) that break independence

Only after confirming the above conditions, matrix multiplication becomes an ideal candidate for parallel execution.

caution

🔁 Always analyze the dependencies in your code before making it parallel. Incorrect assumptions can lead to race conditions, incorrect results, or degraded performance.

Only after confirming the above conditions, matrix multiplication becomes an ideal candidate for parallel execution.

tip

Ideal for Parallelization​

Since all computations are independent:

  • Multiple threads (in CPU) or cores (in GPU) can compute different elements of C simultaneously
  • No need to wait for other values to be computed
  • Massive performance boost using parallel programming

This is why matrix multiplication is often used as a classic example of parallel-friendly algorithms in high-performance computing (HPC), GPU computing, and multi-threaded CPU programming.

3.3 How the CPU Executes Matrix Multiplication in Parallel​

Modern CPUs execute tasks in parallel using several hardware and architectural features that significantly boost performance for data-heavy operations like matrix multiplication:

  • Multiple Cores: Modern processors come with several cores, each capable of executing instructions independently. By dividing a matrix multiplication task among different cores, we can compute multiple portions of the result matrix concurrently.

  • Vectorization (SIMD - Single Instruction, Multiple Data): SIMD instructions allow one instruction to perform the same operation on multiple pieces of data. For example, SIMD can multiply multiple elements from a row of matrix A and a column of matrix B simultaneously, reducing the total number of instructions.

  • Pipelining: CPUs execute instructions in stages (fetch, decode, execute, etc.), and pipelining allows different instructions to be at different stages simultaneously. This overlapping reduces idle time and increases throughput.

Example Strategy for Parallel Matrix Multiplication:​

  • Thread-Level Parallelism: Assign each thread the task of computing a specific cell C[i][j] in the result matrix.
  • SIMD-Level Parallelism: Use SIMD registers to compute multiple partial products (e.g., 4 or 8 floating point operations) in a single instruction.
  • Cache Optimization: Improve data locality to maximize usage of the CPU’s L1/L2 cache, reducing memory access time.

Understanding Data Dependencies in Parallel Execution​

When writing parallel code, understanding data dependencies is critical. These define how instructions relate to each other in terms of the data they read from and write to. Dependencies affect the order in which operations can safely execute, and ignoring them can lead to incorrect results or race conditions.

The Four Types of Data Dependencies and Why They Matter​

  1. True Dependency (Read After Write - RAW)

    • Definition: Instruction B depends on the result of instruction A.
      Example: 
      a = b + c;  // A  
      d = a * e;  // B (depends on result of A)
    • Why it matters: If B executes before A, it uses the wrong or uninitialized value of a, leading to incorrect results.

  2. Anti-Dependency (Write After Read - WAR)

    • Definition: Instruction B writes to a variable that instruction A reads from.
      Example: 
      d = a + b;  // A  
      a = e * f;  // B (writes to 'a' after it's read in A)
    • Why it matters: If B executes before A, the original value of a may be overwritten before it's used, again resulting in incorrect behavior.

  3. Output Dependency (Write After Write - WAW)

    • Definition: Both instructions write to the same variable.
      Example: 
      a = b + c;  // A  
      a = e - f;  // B
    • Why it matters: The final value of a depends on which instruction completes last. Reordering these without synchronization can produce nondeterministic results.

  4. Input Dependency (Read After Read - RAR)

    • Definition: Both instructions read the same variable.
      Example: 
      d = a + b;  // A  
      e = a * f;  // B
    • Why it matters: RAR dependencies generally do not cause correctness issues. However, they can affect cache behavior and performance, especially in systems with non-uniform memory access (NUMA) or weak memory models.

Understanding and handling these dependencies helps:

  • Avoid race conditions and data corruption.
  • Guide safe instruction reordering.
  • Optimize synchronization between threads or vector operations.
  • Improve performance while maintaining correctness in parallelized code.

3.4 Matrix Multiplication Benchmark (Parallel vs Non-Parallel using OpenMP)​

This benchmark compares the performance of non-parallel and OpenMP-parallelized matrix multiplication using std::chrono for precise timing.

✅ Key Features​

  • Matrix size: 1000 x 1000
  • Uses OpenMP to parallelize the multiplication.
  • Uses std::chrono for accurate time measurement in milliseconds.
  • Manually sets OpenMP threads using omp_set_num_threads(4);
  • Compares results for correctness.

4. Common Questions & Challenges in Parallel Computing​

4.1 Limitations of Parallel Computing
note

Parallelism is powerful but not limitless. Understanding its constraints is key.

Common limitations:

  • Diminishing returns: Adding more cores doesn't always lead to proportional speedup.
  • Scalability: Algorithms may not scale well with increasing number of threads.
  • Overheads: Thread creation, synchronization, and context switching.
  • Data races and deadlocks: Increased complexity in debugging and correctness.
  • Memory bottlenecks: Shared resources can become points of contention.

⚠️ Effective parallel computing requires balancing task decomposition, hardware utilization, and code maintainability.

4.2 What Are the Main Challenges in Parallel Computing?
warning

Challenge Accepted – but with trade-offs.

Key challenges:

  • Task decomposition: Breaking a problem into independent units of work is not always straightforward.
  • Load balancing: Ensuring all processors get equal work can be difficult, especially in dynamic or irregular workloads.
  • Communication overhead: Data sharing and synchronization can introduce latency.
  • Debugging complexity: Parallel programs are more prone to concurrency bugs.
  • Hardware heterogeneity: Adapting code to different architectures (e.g., CPUs, GPUs) increases complexity.
4.3 Does Everything Need to be Parallelized?
tip

Short Answer: No. Not every task benefits from parallelism.

Explanation:

  • Parallelization introduces complexity and overhead.
  • Amdahl’s Law limits the potential speedup if a portion of the code remains serial.
  • Some problems are inherently sequential (e.g., recursive dependencies, I/O operations).
  • Poorly parallelized code can lead to lower performance than a well-optimized serial version.
4.4 When Should We Avoid Parallelism?
warning

Avoid parallelism when it introduces more problems than it solves.

Cases to avoid parallelism:

  • Small-scale tasks: Overhead can outweigh the benefits.
  • Highly dependent tasks: Frequent communication or synchronization can create bottlenecks.
  • Limited hardware: No significant gain on single-core or lightly threaded systems.
  • Real-time systems: Deterministic behavior is often more important than speed.
4.5 What Are Common Questions in Parallel Computing?

Q1: Can I convert any serial code into parallel code?
A1: Not always. Some code depends heavily on sequential logic and doesn’t benefit from parallelization.

Q2: Is multi-threading the same as parallelism?
A2: Not exactly. Multi-threading is one form of parallelism, but parallelism can also occur across multiple processors or machines.

Q3: Will parallelism always make my program faster?
A3: No. Due to overheads and limited scalability, it may not lead to speedup and can even slow things down.

Q4: Do I need special hardware for parallel computing?
A4: Depends on the task. Many consumer CPUs support parallelism, but for GPU-based or large-scale parallelism, specialized hardware may help.

1. Introduction to Parallel Programming​

2. Our First Serial Program (Matrix Multiplication)​

3. How We Can Make It Parallel?​