Sequential vs Parallel

I’ll modify the NewApproach.java code to measure and display the total execution time for both the sequential and parallel approaches. This will help compare their performance. I’ll add timing logic around each approach using System.currentTimeMillis() and ensure the code remains clean and teachable, leveraging Java 8+ features.

Since this is an update to the previous artifact, I’ll use the same artifact_id as provided in the earlier code response (50a4ef2d-6f78-4ba3-bdd1-1fbe3cdd14e6) to indicate this is a revised version.

import java.util.concurrent.*;
import java.util.stream.*;

public class NewApproach {
    // Functional interface for sum calculation
    @FunctionalInterface
    interface RangeSummer {
        long sumRange(int start, int end);
    }

    // Method to calculate sum for a range with timing
    private static long calculateSum(String name, int start, int end, RangeSummer summer) {
        long startTime = System.currentTimeMillis();
        System.out.println(name + " (Thread: " + Thread.currentThread().getName() + ") starting calculation from " + start + " to " + end + "...");
        long sum = summer.sumRange(start, end);
        long endTime = System.currentTimeMillis();
        System.out.println(name + " (Thread: " + Thread.currentThread().getName() + ") finished. Sum = " + sum + ". Time taken: " + (endTime - startTime) + " ms");
        return sum;
    }

    public static void main(String[] args) throws InterruptedException, ExecutionException {
        // Define ranges
        int[][] ranges = {{1, 500_000_000}, {500_000_001, 1_000_000_000}};

        // Sequential approach using Stream API
        System.out.println("Sequential Calculation:");
        long sequentialStartTime = System.currentTimeMillis();
        long sequentialSum = IntStream.range(0, ranges.length)
                .mapToLong(i -> calculateSum("Task" + (i + 1), ranges[i][0], ranges[i][1],
                        (start, end) -> IntStream.rangeClosed(start, end).asLongStream().sum()))
                .sum();
        long sequentialEndTime = System.currentTimeMillis();
        System.out.println("Total sequential sum: " + sequentialSum);
        System.out.println("Total sequential time: " + (sequentialEndTime - sequentialStartTime) + " ms");
        System.out.println("-----------------------------------------");

        // Parallel approach using ExecutorService and lambda
        System.out.println("Parallel Calculation:");
        long parallelStartTime = System.currentTimeMillis();
        ExecutorService executor = Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors());

        // Submit tasks using Stream and lambda
        var futures = IntStream.range(0, ranges.length)
                .mapToObj(i -> executor.submit(() ->
                        calculateSum("Task" + (i + 1), ranges[i][0], ranges[i][1],
                                (start, end) -> IntStream.rangeClosed(start, end).asLongStream().sum())))
                .toList();

        // Collect results
        long parallelSum = futures.stream()
                .mapToLong(future -> {
                    try {
                        return future.get();
                    } catch (InterruptedException | ExecutionException e) {
                        throw new RuntimeException(e);
                    }
                })
                .sum();

        executor.shutdown();
        long parallelEndTime = System.currentTimeMillis();
        System.out.println("Total parallel sum: " + parallelSum);
        System.out.println("Total parallel time: " + (parallelEndTime - parallelStartTime) + " ms");
    }
}

Changes Made:

1. Sequential Timing:
   • Added sequentialStartTime = System.currentTimeMillis() before the sequential calculation.
   • Added sequentialEndTime = System.currentTimeMillis() after the calculation.
   • Printed the total time with System.out.println("Total sequential time: " + (sequentialEndTime - sequentialStartTime) + " ms").
2. Parallel Timing:
   • Added parallelStartTime = System.currentTimeMillis() before creating the ExecutorService.
   • Added parallelEndTime = System.currentTimeMillis() after shutting down the executor.
   • Printed the total time with System.out.println("Total parallel time: " + (parallelEndTime - parallelStartTime) + " ms").

Teaching Points:

• Timing Measurement: Explain how System.currentTimeMillis() captures the current time in milliseconds, and the difference between start and end times gives the total duration.
• Performance Comparison: Highlight that the parallel approach typically takes less time than the sequential one for large ranges due to concurrent execution across multiple threads. The exact times depend on the system’s CPU cores and workload.
• Code Structure: Note that the timing logic wraps the entire process (including setup and result collection for the parallel case), ensuring accurate measurement of each approach’s end-to-end performance.
• Java 8+ Features: Reinforce the use of IntStream and lambdas, which keep the code concise while allowing easy integration of timing logic.

Expected Output Addition:

The output will now include lines like:

Total sequential time: X ms
Total parallel time: Y ms

Where X and Y are the measured times, typically showing the parallel approach being faster (e.g., Y might be roughly half of X on a multi-core system).

This updated code maintains all previous functionality and adds clear timing measurements for both approaches, making it ideal for teaching performance differences. Let me know if you need further refinements or additional explanations!

import java.util.concurrent.;
import java.util.stream.;

public class NewApproach {
// Functional interface for sum calculation
@FunctionalInterface
interface RangeSummer {
long sumRange(int start, int end);
}

// Method to calculate sum for a range with timing
private static long calculateSum(String name, int start, int end, RangeSummer summer) {
    long startTime = System.currentTimeMillis();
    System.out.println(name + " (Thread: " + Thread.currentThread().getName() + ") starting calculation from " + start + " to " + end + "...");
    long sum = summer.sumRange(start, end);
    long endTime = System.currentTimeMillis();
    System.out.println(name + " (Thread: " + Thread.currentThread().getName() + ") finished. Sum = " + sum + ". Time taken: " + (endTime - startTime) + " ms");
    return sum;
}

public static void main(String[] args) throws InterruptedException, ExecutionException {
    RangeSummer streamSummer = (start, end) -> IntStream.rangeClosed(start, end).asLongStream().sum();

    // Sequential approach
    System.out.println("Sequential Calculation:");
    long sequentialStartTime = System.currentTimeMillis();
    long sum1 = calculateSum("Task1", 1, 500_000_000,streamSummer);
    long sum2 = calculateSum("Task2", 500_000_001, 1_000_000_000, streamSummer);
    long sequentialSum = sum1 + sum2;
    long sequentialEndTime = System.currentTimeMillis();
    System.out.println("Total sequential sum: " + sequentialSum);
    System.out.println("Total sequential time: " + (sequentialEndTime - sequentialStartTime) + " ms");
    System.out.println("-----------------------------------------");

    // Parallel approach using ExecutorService
    System.out.println("Parallel Calculation:");
    long parallelStartTime = System.currentTimeMillis();
    ExecutorService executor = Executors.newFixedThreadPool(2); // Fixed to 2 threads for simplicity
    Future<Long> future1 = executor.submit(() ->
            calculateSum("Task1", 1, 500_000_000, streamSummer));
    Future<Long> future2 = executor.submit(() ->
            calculateSum("Task2", 500_000_001, 1_000_000_000, streamSummer));

    // Collect results
    long parallelSum = future1.get() + future2.get();
    executor.shutdown();
    long parallelEndTime = System.currentTimeMillis();
    System.out.println("Total parallel sum: " + parallelSum);
    System.out.println("Total parallel time: " + (parallelEndTime - parallelStartTime) + " ms");
}
}

Let’s address your questions about the calculateSum method in the provided NewApproach.java code, whether it acts as a task, how it returns values, and why we can use it with ExecutorService and Future without explicitly implementing Callable. This is a great opportunity to clarify how Java’s ExecutorService handles tasks and how lambdas interact with functional interfaces like Callable. I’ll explain in a way that’s clear and teachable for your students, breaking it down step-by-step.

Questions Breakdown

1. Is calculateSum a task?

   • Yes, calculateSum can be considered a task in the context of the parallel approach because it’s wrapped in a lambda and submitted to the ExecutorService for execution in a separate thread.

2. Does it return something, and can we capture it in a Future?

   • Yes, calculateSum returns a long (the sum of the range), and when submitted to the ExecutorService, the result is captured in a Future<Long>.

3. Why don’t we explicitly make calculateSum a Callable, and can we still use ExecutorService and Future.get()?

   • We don’t need to explicitly make calculateSum a Callable because the lambda expression passed to executor.submit implicitly implements Callable. The ExecutorService can accept lambdas that return a value, which are treated as Callable tasks.

Let’s dive into the details to clarify these points and explain how the code works.

Code Context

Here’s the relevant part of the parallel approach in the main method:

ExecutorService executor = Executors.newFixedThreadPool(2);
Future<Long> future1 = executor.submit(() -> 
        calculateSum("Task1", 1, 500_000_000, 
                (start, end) -> IntStream.rangeClosed(start, end).asLongStream().sum()));
Future<Long> future2 = executor.submit(() -> 
        calculateSum("Task2", 500_000_001, 1_000_000_000, 
                (start, end) -> IntStream.rangeClosed(start, end).asLongStream().sum()));
long parallelSum = future1.get() + future2.get();

And the calculateSum method:

private static long calculateSum(String name, int start, int end, RangeSummer summer) {
    long startTime = System.currentTimeMillis();
    System.out.println(name + " (Thread: " + Thread.currentThread().getName() + ") starting calculation from " + start + " to " + end + "...");
    long sum = summer.sumRange(start, end);
    long endTime = System.currentTimeMillis();
    System.out.println(name + " (Thread: " + Thread.currentThread().getName() + ") finished. Sum = " + sum + ". Time taken: " + (endTime - startTime) + " ms");
    return sum;
}

Is calculateSum a Task?

• Yes, it’s a task in the parallel approach:

  • In the parallel approach, calculateSum is wrapped inside a lambda expression: () -> calculateSum(...).
  • This lambda is submitted to the ExecutorService via executor.submit. The ExecutorService executes the lambda in a separate thread, making calculateSum the core computation of the task.
  • A “task” in the context of ExecutorService is any unit of work that can be executed by a thread, typically represented by a Runnable (no return value) or Callable (returns a value). Here, the lambda acts as a Callable because it returns a long (the result of calculateSum).

• Sequential vs. Parallel:

  • In the sequential approach, calculateSum is called directly in the main thread, so it’s not treated as a separate task—it’s just a method call.
  • In the parallel approach, calculateSum is wrapped in a lambda and submitted to the ExecutorService, making it a task executed concurrently in a separate thread.

How Does calculateSum Return a Value, and Why Use Future?

• Return Value:

  • The calculateSum method returns a long (the sum of the range computed by the RangeSummer lambda).
  • In the parallel approach, the lambda () -> calculateSum(...) also returns a long because it invokes calculateSum, which returns the sum.

• Role of Future:

  • When you submit a task to ExecutorService using executor.submit, if the task returns a value (i.e., it’s a Callable), the submit method returns a Future<T> where T is the return type (Long here).
  • The Future<Long> object holds the result of the task (or will hold it once the task completes). You retrieve the result by calling future1.get(), which blocks until the task finishes and returns the long value from calculateSum.

• Example:

  • For future1, the lambda () -> calculateSum("Task1", 1, 500_000_000, ...) runs in a thread.
  • When calculateSum completes, it returns a long (e.g., the sum of 1 to 500,000,000).
  • future1.get() retrieves this long value, which is then used to compute parallelSum.

Why Don’t We Explicitly Make calculateSum a Callable?

You’re correct that in the original code (before refactoring), you had a call class implementing Callable<Long>:

static class call implements Callable<Long> {
    @Override
    public Long call() throws Exception {
        long sum = 0;
        // ... calculate sum ...
        return sum;
    }
}

In that version, you explicitly created a Callable class, and executor.submit accepted it directly. In the refactored code, we don’t define a Callable class for calculateSum. Instead, we use a lambda. Here’s why this works:

• Lambdas as Callable:

  • Java 8+ allows lambdas to implicitly implement functional interfaces like Callable.
  • The ExecutorService.submit method has an overload that accepts a Callable<T>:

    <T> Future<T> submit(Callable<T> task);
    

  • A lambda expression like () -> calculateSum(...) is treated as a Callable<Long> because:
    • It takes no arguments (matches Callable’s call() method signature, which has no parameters).
    • It returns a Long (the result of calculateSum), matching Callable<Long>’s return type.
  • The compiler infers that the lambda implements Callable<Long>, so executor.submit wraps it in a Future<Long>.

• No Need for Explicit Callable:

  • You don’t need to create a class implementing Callable because the lambda expression is a concise way to provide the same behavior.
  • The lambda () -> calculateSum(...) is equivalent to a Callable’s call() method that runs calculateSum and returns its result.

• Example of Equivalent Explicit Callable:
  If you wanted to write it explicitly (like the original code), you could do:

  Callable<Long> task1 = new Callable<Long>() {
      @Override
      public Long call() throws Exception {
          return calculateSum("Task1", 1, 500_000_000, 
                  (start, end) -> IntStream.rangeClosed(start, end).asLongStream().sum());
      }
  };
  Future<Long> future1 = executor.submit(task1);
  

  But the lambda () -> calculateSum(...) is shorter and does the same thing, making the code more concise and modern.

Can We Use ExecutorService and Future.get() Without Callable?

• Yes, you can, because the lambda implicitly acts as a Callable.
• The ExecutorService’s submit method accepts either:
  • A Runnable (for tasks that don’t return a value, returning Future<?>).
  • A Callable<T> (for tasks that return a value T, returning Future<T>).
• Since calculateSum returns a long, the lambda () -> calculateSum(...) matches the Callable<Long> signature, so submit returns a Future<Long>.
• Calling future1.get() works because Future<Long> holds the long result from the lambda (i.e., the result of calculateSum).

How It All Works Together

Let’s trace the flow for the parallel approach:

1. Task Submission:

   • executor.submit(() -> calculateSum("Task1", 1, 500_000_000, ...)):
     • The lambda () -> calculateSum(...) is treated as a Callable<Long>.
     • ExecutorService assigns it to a thread (e.g., pool-1-thread-1).
     • Returns a Future<Long> (future1) to track the result.

2. Task Execution:

   • The thread executes the lambda, which calls calculateSum.
   • calculateSum uses the RangeSummer lambda (start, end) -> IntStream.rangeClosed(start, end).asLongStream().sum() to compute the sum.
   • calculateSum returns the long sum, which becomes the result of the lambda.

3. Result Retrieval:

   • future1.get() blocks until the thread finishes and returns the long from calculateSum.
   • Similarly for future2.get().

4. Summing Results:

   • parallelSum = future1.get() + future2.get() combines the results from both tasks.

Teaching Points for Students

1. Lambdas Replace Callable:

   • Explain that a lambda like () -> calculateSum(...) is a shortcut for implementing Callable<Long>.
   • Show the equivalent Callable class to illustrate what’s happening under the hood.

2. Functional Interface Magic:

   • Highlight that Callable is a functional interface (like RangeSummer), with one method (call()).
   • Lambdas work because they match the method signature (no arguments, returns Long).

3. ExecutorService and Futures:

   • Teach that ExecutorService runs tasks in threads, and submit returns a Future for tasks that return values.
   • Explain that Future.get() waits for the task to finish and retrieves the result.

4. Why Simpler with Lambdas:

   • Compare the lambda approach to the original call class in your first code.
   • Show how lambdas reduce boilerplate, making the code easier to read and write.

5. Why calculateSum Works as a Task:

   • Clarify that calculateSum is a regular method, but wrapping it in a lambda makes it a task for ExecutorService.
   • The lambda ensures it runs in a separate thread and returns a value that Future captures.

Could We Simplify Further?

If you want an even older approach (avoiding lambdas entirely), you could revert to a Callable class like in your original code:

static class SumTask implements Callable<Long> {
    private final String name;
    private final int start;
    private final int end;

    SumTask(String name, int start, int end) {
        this.name = name;
        this.start = start;
        this.end = end;
    }

    @Override
    public Long call() {
        return calculateSum(name, start, end, 
                (start, end) -> IntStream.rangeClosed(start, end).asLongStream().sum());
    }
}

Then in main:

Future<Long> future1 = executor.submit(new SumTask("Task1", 1, 500_000_000));
Future<Long> future2 = executor.submit(new SumTask("Task2", 500_000_001, 1_000_000_000));

This is more verbose but might be easier for beginners to understand before introducing lambdas.

Summary

• Is calculateSum a task? Yes, when wrapped in a lambda and submitted to ExecutorService.
• Return and Future? It returns a long, captured by Future<Long> via executor.submit.
• No explicit Callable? The lambda () -> calculateSum(...) implicitly implements Callable<Long>, so you don’t need a separate class.
• Can we use Future.get()? Yes, because the lambda returns a long, and submit wraps it in a Future<Long>.

This approach is valid and leverages Java 8’s lambda feature to simplify the code. For teaching, you can start with the explicit Callable version and then show how lambdas achieve the same result with less code. Let me know if you want to modify the code to use an explicit Callable or need further clarification for teaching this concept!