diff --git a/allhands/spring2025/weekeleven/teamone/index.qmd b/allhands/spring2025/weekeleven/teamone/index.qmd new file mode 100644 index 0000000..cd1bb31 --- /dev/null +++ b/allhands/spring2025/weekeleven/teamone/index.qmd @@ -0,0 +1,978 @@ +--- +author: [Javier Bejarano Jimenez, Finley Banas, Joseph Oforkansi, Anupraj Guragain, Anton Hedlund] +title: What is the performance difference, measured in time when running a doubling experiment between a SLL queue, a DLL queue and a queue built with an array based list when performing basic operations? +page-layout: full +categories: [post, queues, linked lists, doubling experiment] +date: "2025-03-28" +date-format: long +toc: true +--- + +# Introduction + +Data structures play a critical role in efficient software development, influencing performance, scalability, and system responsiveness. Among them, queues are fundamental, powering applications such as task scheduling, messaging systems, and real-time data processing. In this project, our team explored three key queue implementations—Singly Linked List (SLL), Doubly Linked List (DLL), and Array-based Queue. This project seeks to answer our research question: What are the performance differences between SLL queue, DLL queue, and array-based queue implementations when executing basic operations (addfirst, addlast, removefirst, removelast, add (+), and iadd (+=)?. We analyzed their performance through benchmarking experiments using SystemSense. + +As algorithm engineers tackling this project we considered multiple aspects, including: + +Algorithmic Complexity: Understanding the time and space complexity of queue operations to determine trade-offs between different implementations. + +Memory Management: Evaluating how memory allocation and deallocation affect performance, particularly in linked list-based vs. array-based implementations. + +Concurrency Considerations: Investigating how these data structures behave in multi-threaded environments where multiple processes access and modify queues simultaneously. + +Use Case Optimization: Identifying practical applications where each queue implementation excels, such as high-throughput systems, real-time event processing, and low-latency applications. + +Benchmarking Methodology: Designing experiments to measure execution times, analyze scaling behavior, and compare performance under different workloads. + +Through this project, we aim to provide insights into the efficiency of these queue implementations and guide the selection of an optimal data structure based on application requirements. By profiling and analyzing queue operations, we not only enhance our understanding of core data structures but also develop practical skills in performance analysis, a crucial skill in algorithm engineering and systems design. + +## Motivation + +Efficient data structures are essential in software development, especially when dealing with queues in real-world applications such as scheduling systems, task management, and networking. Different queue implementations like Singly Linked List (SLL), Doubly Linked List (DLL), and Array-based Queue offer trade-offs in terms of performance. Our project aims to benchmark these tradeoffs and analyze the data by comparing the execution times of the queue operations. + +# Queue Implementations Analysis + +## Queue Structure and FIFO Principle + +Queues follow the First-In-First-Out (FIFO) principle where elements are added at the rear and removed from the front, ensuring sequential processing order. + +## Implementations Overview + +This project explores three queue implementations: + +1. **Singly Linked List (SLL) Queue** + - Uses one-directional nodes with `next` references + - Maintains both head and tail pointers for efficient operations + - Each node stores only the value and next reference + +2. **Doubly Linked List (DLL) Queue** + - Uses bidirectional nodes with both `prev` and `next` references + - Maintains both head and tail pointers + - Each node stores value, previous, and next references + +3. **Array-based Queue** + - No explicit node structure, just a container of elements + - Optimized for operations at both ends + +## Key Operations + +All implementations support these core operations: +- `enqueue`: Add element to the rear (O(1) in all implementations) +- `dequeue`: Remove element from the front (O(1) in all implementations) +- `peek`: View front element without removing (O(1) in all implementations) +- `__add__`: Concatenate queues (O(1) in linked lists, O(n) in array-based) +- `__iadd__`: In-place concatenation (O(1) in linked lists, O(n) in array-based) + +### Key Implementation Examples + +#### Enqueue Operation (SLL) + +```python +def enqueue(self, value: Any) -> None: + """Add an element to the end of the queue. O(1) operation using tail pointer.""" + new_node = Node(value) + if self.is_empty(): + self.head = new_node + else: + self.tail.next = new_node # Directly append at tail + self.tail = new_node # Update tail pointer + self.size += 1 +``` + +#### Dequeue Operation (DLL) + +```python +def dequeue(self) -> Any: + """Remove and return the first element from the queue. O(1) operation.""" + if self.is_empty(): + raise IndexError("Queue is empty") + value = self.head.value + self.head = self.head.next + if self.head is None: + self.tail = None + else: + self.head.prev = None + self.size -= 1 + return value +``` + +#### Queue Concatenation (Array-based) + +```python +def __add__(self, other: "ListQueueDisplay") -> "ListQueueDisplay": + """Concatenate two queues. O(n) operation.""" + result = ListQueueDisplay() + result.items = deque(self.items) # Copy first queue + result.items.extend(other.items) # Append second queue + return result +``` + +## Implementation Considerations + +### SLL Queue Considerations +- Simpler structure with less memory overhead per node +- Forward-only traversal limits some operations +- Efficient concatenation due to tail pointer + +### DLL Queue Considerations +- Bidirectional links enable more flexible operations +- Higher memory usage due to extra pointer per node +- Supports easy traversal in both directions + +### Array-based Queue Considerations +- No manual pointer management needed +- Leverages efficient implementation of Python's `deque` +- Internal array may require occasional reallocation + +### Benchmarking + +There are two main benchmarking function in our project. + +#### Basic analysis + +```python +def analyze_queue(queue_class, size=1000): + """Analyze a queue implementation.""" + approach = next( + (k for k, v in QUEUE_IMPLEMENTATIONS.items() if v == queue_class), None + ) + if approach is None: + console.print("[red]Unknown queue implementation[/red]") + return + + console.print(f"\n{approach.value.upper()} Queue Implementation") + + try: + queue = queue_class() + operations = [] + + # Test enqueue + enqueue_time = time_operation(lambda: [queue.enqueue(i) for i in range(size)]) + operations.append(("enqueue", enqueue_time, size)) + + # Test dequeue + dequeue_count = size // 2 + dequeue_time = time_operation( + lambda: [queue.dequeue() for _ in range(dequeue_count)] + ) + operations.append(("dequeue", dequeue_time, dequeue_count)) + + # Refill queue + for i in range(dequeue_count): + queue.enqueue(i) + + # Test peek + peek_count = size // 3 + peek_time = time_operation(lambda: [queue.peek() for _ in range(peek_count)]) + operations.append(("peek", peek_time, peek_count)) + + # Test concat + other = queue_class() + for i in range(size // 10): + other.enqueue(i) + concat_time = time_operation(lambda: queue + other) + operations.append(("concat", concat_time, size // 10)) + + # Test iconcat + iconcat_time = time_operation(lambda: queue.__iadd__(other)) + operations.append(("iconcat", iconcat_time, size // 10)) + + # Display results in table + table = Table( + title=f"{approach.value.upper()} Queue Performance Analysis", + box=box.ROUNDED, + show_header=True, + header_style="bold magenta", + ) + table.add_column("Operation", style="cyan") + table.add_column("Time (ms)", justify="right") + table.add_column("Elements", justify="right") + table.add_column("Time/Element (ms)", justify="right") + + for operation, time_taken, elements in operations: + time_per_element = time_taken / elements if elements > 0 else 0 + table.add_row( + operation, + f"{time_taken * 1000:.6f}", # Convert to milliseconds + f"{elements:,}", + f"{time_per_element * 1000:.6f}", # Convert to milliseconds + ) + + console.print(Panel(table)) + + except Exception as e: + console.print(f"[red]Error testing {approach.value}: {str(e)}[/red]") + import traceback + + console.print(traceback.format_exc()) +``` + +This function performs a basic performance analysis with the following operations: +- Enqueue: Adds size elements to the queue +- Dequeue: Removes size/2 elements +- Peek: Looks at size/3 elements without removing them +- Concat: Concatenates with another queue of size size/10 +- Iconcat: In-place concatenation with another queue of size size/10 + +#### Doubling experiment + +```python + +def doubling( + initial_size: int = typer.Option(100, help="Initial size for doubling experiment"), + max_size: int = typer.Option(1000, help="Maximum size for doubling experiment"), + dll: bool = typer.Option(True, help="Test DLL implementation"), + sll: bool = typer.Option(True, help="Test SLL implementation"), + array: bool = typer.Option(True, help="Test Array implementation"), +): + """Run doubling experiment on queue implementations.""" + # Create results directory if it doesn't exist + results_dir = Path("results") + results_dir.mkdir(exist_ok=True) + + sizes = [] + current_size = initial_size + while current_size <= max_size: + sizes.append(current_size) + current_size *= 2 + + # Dictionary to store all results for plotting + all_results = {} + + for approach, queue_class in QUEUE_IMPLEMENTATIONS.items(): + if not ( + (approach == QueueApproach.dll and dll) + or (approach == QueueApproach.sll and sll) + or (approach == QueueApproach.array and array) + ): + continue + + try: + console.print(f"\n{approach.value.upper()} Queue Implementation") + results = { + "enqueue": [], + "dequeue": [], + "peek": [], + "concat": [], + "iconcat": [], + } + + for size in sizes: + queue = queue_class() + + # Enqueue + enqueue_time = time_operation( + lambda: [queue.enqueue(i) for i in range(size)] + ) + results["enqueue"].append(enqueue_time) + + # Dequeue + dequeue_time = time_operation( + lambda: [queue.dequeue() for _ in range(size // 2)] + ) + results["dequeue"].append(dequeue_time) + + # Refill queue + for i in range(size // 2): + queue.enqueue(i) + + # Peek + peek_time = time_operation( + lambda: [queue.peek() for _ in range(size // 3)] + ) + results["peek"].append(peek_time) + + # Concat + other = queue_class() + for i in range(size // 10): + other.enqueue(i) + + concat_time = time_operation(lambda: queue + other) + results["concat"].append(concat_time) + + # Iconcat + iconcat_time = time_operation(lambda: queue.__iadd__(other)) + results["iconcat"].append(iconcat_time) + + # Store results for plotting + all_results[approach.value] = results + + # Display results in table + table = Table( + title=f"{approach.value.upper()} Queue Doubling Experiment Results", + box=box.ROUNDED, + show_header=True, + header_style="bold magenta", + ) + table.add_column("Size (n)", justify="right") + for operation in results.keys(): + table.add_column(operation, justify="right") + + for i, size in enumerate(sizes): + row = [f"{size:,}"] + for operation in results.keys(): + value = results[operation][i] + if np.isnan(value): # Check for NaN + row.append("N/A") + else: + row.append(f"{value * 1000:.6f}") # Convert to milliseconds + table.add_row(*row) + + console.print(Panel(table)) + + except Exception as e: + console.print(f"[red]Error testing {approach.value}: {str(e)}[/red]") + import traceback + + console.print(traceback.format_exc()) + + # Generate and save plots + plot_results(sizes, all_results, results_dir) + console.print(f"[green]Plots saved to [bold]{results_dir}[/bold] directory[/green]") + +``` + +This doubling experiment does the following +- Starts with initial_size and doubles the size until reaching max_size +- For each size, measures the same operations as the basic analysis +- Generates plots to visualize the results + +#### Key benchmarking feature + +##### Timing Mechanism + +```python + +def time_operation(func): + """Time an operation using high-precision counter.""" + try: + # Warm up + func() + + # Actual timing + start_time = perf_counter() + func() + elapsed = perf_counter() - start_time + return elapsed + except Exception as e: + console.print(f"[red]Error during operation: {str(e)}[/red]") + return float("nan") +``` + +- Uses perf_counter() for high-precision timing +- Includes a warm-up run to avoid cold-start penalties +- Returns elapsed time in seconds + +##### Result Visualization + +```python +def plot_results(sizes, all_results, results_dir): + """Generate and save plots for doubling experiment results.""" + operations = ["enqueue", "dequeue", "peek", "concat", "iconcat"] + + # Create log-log plots for each operation (keeping only these, removing regular operation plots) + for operation in operations: + # Skip regular plots for operations - only create log-log plots + if len(sizes) > 2: # Only create log plots if we have enough data points + plt.figure(figsize=(10, 6)) + + for impl, results in all_results.items(): + times = np.array(results[operation]) * 1000 # Convert to milliseconds + if np.all(times > 0): # Avoid log(0) + plt.loglog( + sizes, times, marker="o", label=f"{impl.upper()}", linewidth=2 + ) + + # Add reference lines for O(1), O(n), O(n²) + x_range = np.array(sizes) + # Add O(1) reference + plt.loglog( + x_range, np.ones_like(x_range) * times[0], "--", label="O(1)", alpha=0.5 + ) + # Add O(n) reference - scale to fit + plt.loglog( + x_range, + x_range * (times[0] / x_range[0]), + "--", + label="O(n)", + alpha=0.5, + ) + # Add O(n²) reference - scale to fit + plt.loglog( + x_range, + np.power(x_range, 2) * (times[0] / np.power(x_range[0], 2)), + "--", + label="O(n²)", + alpha=0.5, + ) + + plt.title( + f"Log-Log Plot for {operation.capitalize()} Operation", fontsize=16 + ) + plt.xlabel("Log Queue Size", fontsize=14) + plt.ylabel("Log Time (ms)", fontsize=14) + plt.grid(True, which="both", linestyle="--", alpha=0.5) + plt.legend(fontsize=12) + plt.tight_layout() + + # Save log-log plot + log_plot_path = results_dir / f"{operation}_loglog_plot.png" + plt.savefig(log_plot_path) + plt.close() + + # Create regular performance plots for each implementation (keeping these, removing log-scale implementation plots) + for impl, results in all_results.items(): + plt.figure(figsize=(10, 6)) + + for operation in operations: + times = np.array(results[operation]) * 1000 # Convert to milliseconds + plt.plot(sizes, times, marker="o", label=operation, linewidth=2) + + plt.title(f"{impl.upper()} Queue Implementation Performance", fontsize=16) + plt.xlabel("Queue Size (n)", fontsize=14) + plt.ylabel("Time (ms)", fontsize=14) + plt.grid(True, linestyle="--", alpha=0.7) + plt.legend(fontsize=12) + plt.tight_layout() + + # Save plot + plot_path = results_dir / f"{impl}_performance.png" + plt.savefig(plot_path) + plt.close() +``` +- Creates log-log plots for each operation to show algorithmic complexity +- Generates regular performance plots for each implementation +- Saves all plots to a results directory + +##### Error Handling + +- Gracefully handles exceptions during benchmarking +- Report errors with detailed tracebacks +- Continues testing other implementations if one fails + +##### Output Format: + +- Uses Rich library for formatted console output +- Displays results in tables with: + - Operation name + - Time taken (in milliseconds) + - Number of elements + - Time per element + +#### References + +- AI agent of Cursor AI (Claude sonnet 3.7) was heavily used for the information, generation, debugging and implementaion of various code specifically main.py +- https://algorithmology.org/ +- https://algorithmology.org/schedule/weekseven/ +- https://www.geeksforgeeks.org/queue-in-python/ +- https://stackoverflow.com/questions/45688871/implementing-an-efficient-queue-in-python +- Chatgpt for information +- Deepseek for information +- Qwen for information + +## Running and Using the Tool + +The benchmarking supports three queue implementations: +- DLL (Doubly Linked List) +- SLL (Singly Linked List) +- Array-based Queue + +### Setting Up + +To run the benchmarking tool, ensure you have Poetry installed onto your device. Navigate to the project directory and install dependencies if you have not already: + +`cd analyze && poetry install` + +### Running the Experiments + +The tool provides two main benchmarking experiments which can also be access by + +`poetry run analyze --help` + +#### Doubling Experiment + +To run the doubling experiment, execute: + +`poetry run analyze doubling` + +This experiment measures how performance will scale with the increasing input sizes. + +You can also run: +`poetry run analyze doubling --help` +for more details and detailed apporach + +#### Implementation Performance Analysis + +To analyze the performance of individual queue operations, run: + +`poetry run analyze analyze` + +This command will provide execution times for operations like `peek`, `dequeue`, and `enqueue` to compare their efficiency. + +You can also run: +`poetry run analyze analyze --help` +for more details and detailed apporach + +## Output Analysis + +### Anton Hedlund + +#### Run of systemsense + +```cmd +poetry run systemsense completeinfo +Displaying System Information + +╭─────────────────────────────────────────────────────── System Information ───────────────────────────────────────────────────────╮ +│ ╭──────────────────┬───────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ +│ │ System Parameter │ Parameter Value │ │ +│ ├──────────────────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ +│ │ battery │ 79.00% battery life remaining, 6:15:00 seconds remaining │ │ +│ │ cpu │ arm │ │ +│ │ cpucores │ 11 cores │ │ +│ │ cpufrequencies │ Min: Unknown Mhz, Max: Unknown Mhz │ │ +│ │ datetime │ 2025-03-26 22:32:34.509801 │ │ +│ │ disk │ Using 10.39 GB of 460.43 GB │ │ +│ │ hostname │ MacBook-Pro-Anton.local │ │ +│ │ memory │ Using 6.58 GB of 18.00 GB │ │ +│ │ platform │ macOS-15.3.2-arm64-arm-64bit │ │ +│ │ pythonversion │ 3.12.8 │ │ +│ │ runningprocesses │ 594 running processes │ │ +│ │ swap │ Using 0.49 GB of 2.00 GB │ │ +│ │ system │ Darwin │ │ +│ │ systemload │ Average Load: 2.82, CPU Utilization: 20.50% │ │ +│ │ virtualenv │ /Users/antonhedlund/Compsci/algorhytms_202/computer-science-202-algorithm-engineering-project-1-ahedlund… │ │ +│ ╰──────────────────┴───────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │ +╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +Displaying Benchmark Results + +╭────────────────────────────────────────────────────────────────────────── Benchmark Results ──────────────────────────────────────────────────────────────────────────╮ +│ ╭────────────────┬─────────────────────────────────────────────────────────────────╮ │ +│ │ Benchmark Name │ Benchmark Results (sec) │ │ +│ ├────────────────┼─────────────────────────────────────────────────────────────────┤ │ +│ │ addition │ [0.37942662500427105, 0.38371645798906684, 0.39661604099092074] │ │ +│ │ concatenation │ [1.831420500006061, 1.8045542500040028, 1.8012452079856303] │ │ +│ │ exponentiation │ [2.1522245419910178, 2.1751532499911264, 2.2064731669961475] │ │ +│ │ multiplication │ [0.4023984170053154, 0.45870250000734814, 0.5052193750161678] │ │ +│ │ rangelist │ [0.10194725001929328, 0.09878037497401237, 0.10006220897776075] │ │ +│ ╰────────────────┴─────────────────────────────────────────────────────────────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +``` + +#### Run of Doubling Experiment + +```bash +DLL Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ DLL Queue Doubling Experiment Results │ +│ ╭──────────┬──────────┬──────────┬──────────┬──────────┬──────────╮ │ +│ │ Size (n) │ enqueue │ dequeue │ peek │ concat │ iconcat │ │ +│ ├──────────┼──────────┼──────────┼──────────┼──────────┼──────────┤ │ +│ │ 100 │ 0.023667 │ 0.006667 │ 0.001916 │ 0.000333 │ 0.000417 │ │ +│ │ 200 │ 0.073833 │ 0.013459 │ 0.003500 │ 0.000208 │ 0.000167 │ │ +│ │ 400 │ 0.115250 │ 0.024208 │ 0.006083 │ 0.000125 │ 0.000125 │ │ +│ │ 800 │ 0.200166 │ 0.048375 │ 0.011542 │ 0.000167 │ 0.000166 │ │ +│ ╰──────────┴──────────┴──────────┴──────────┴──────────┴──────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +SLL Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ SLL Queue Doubling Experiment Results │ +│ ╭──────────┬──────────┬──────────┬──────────┬──────────┬──────────╮ │ +│ │ Size (n) │ enqueue │ dequeue │ peek │ concat │ iconcat │ │ +│ ├──────────┼──────────┼──────────┼──────────┼──────────┼──────────┤ │ +│ │ 100 │ 0.022000 │ 0.005958 │ 0.002292 │ 0.000958 │ 0.000500 │ │ +│ │ 200 │ 0.040667 │ 0.011459 │ 0.003500 │ 0.000417 │ 0.000209 │ │ +│ │ 400 │ 0.099958 │ 0.020958 │ 0.006125 │ 0.000333 │ 0.000208 │ │ +│ │ 800 │ 0.169250 │ 0.041334 │ 0.011708 │ 0.000333 │ 0.000209 │ │ +│ ╰──────────┴──────────┴──────────┴──────────┴──────────┴──────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +ARRAY Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ ARRAY Queue Doubling Experiment Results │ +│ ╭──────────┬──────────┬──────────┬──────────┬──────────┬──────────╮ │ +│ │ Size (n) │ enqueue │ dequeue │ peek │ concat │ iconcat │ │ +│ ├──────────┼──────────┼──────────┼──────────┼──────────┼──────────┤ │ +│ │ 100 │ 0.003791 │ 0.003334 │ 0.002459 │ 0.001833 │ 0.000250 │ │ +│ │ 200 │ 0.007083 │ 0.006166 │ 0.004667 │ 0.001917 │ 0.000208 │ │ +│ │ 400 │ 0.014125 │ 0.013125 │ 0.009208 │ 0.003375 │ 0.000292 │ │ +│ │ 800 │ 0.027542 │ 0.026292 │ 0.017792 │ 0.006417 │ 0.000375 │ │ +│ ╰──────────┴──────────┴──────────┴──────────┴──────────┴──────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +``` + +#### Run of Performance Analysis + +```bash +oetry run analyze analyze + +DLL Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ DLL Queue Performance Analysis │ +│ ╭───────────┬───────────┬──────────┬───────────────────╮ │ +│ │ Operation │ Time (ms) │ Elements │ Time/Element (ms) │ │ +│ ├───────────┼───────────┼──────────┼───────────────────┤ │ +│ │ enqueue │ 0.258167 │ 1,000 │ 0.000258 │ │ +│ │ dequeue │ 0.060583 │ 500 │ 0.000121 │ │ +│ │ peek │ 0.015292 │ 333 │ 0.000046 │ │ +│ │ concat │ 0.000334 │ 100 │ 0.000003 │ │ +│ │ iconcat │ 0.000417 │ 100 │ 0.000004 │ │ +│ ╰───────────┴───────────┴──────────┴───────────────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +SLL Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ SLL Queue Performance Analysis │ +│ ╭───────────┬───────────┬──────────┬───────────────────╮ │ +│ │ Operation │ Time (ms) │ Elements │ Time/Element (ms) │ │ +│ ├───────────┼───────────┼──────────┼───────────────────┤ │ +│ │ enqueue │ 0.196000 │ 1,000 │ 0.000196 │ │ +│ │ dequeue │ 0.050458 │ 500 │ 0.000101 │ │ +│ │ peek │ 0.014625 │ 333 │ 0.000044 │ │ +│ │ concat │ 0.000791 │ 100 │ 0.000008 │ │ +│ │ iconcat │ 0.000500 │ 100 │ 0.000005 │ │ +│ ╰───────────┴───────────┴──────────┴───────────────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +ARRAY Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ ARRAY Queue Performance Analysis │ +│ ╭───────────┬───────────┬──────────┬───────────────────╮ │ +│ │ Operation │ Time (ms) │ Elements │ Time/Element (ms) │ │ +│ ├───────────┼───────────┼──────────┼───────────────────┤ │ +│ │ enqueue │ 0.043708 │ 1,000 │ 0.000044 │ │ +│ │ dequeue │ 0.029083 │ 500 │ 0.000058 │ │ +│ │ peek │ 0.019541 │ 333 │ 0.000059 │ │ +│ │ concat │ 0.007208 │ 100 │ 0.000072 │ │ +│ │ iconcat │ 0.000625 │ 100 │ 0.000006 │ │ +│ ╰───────────┴───────────┴──────────┴───────────────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +``` + +### Anupraj Guragain + +#### Run of systemsense + +```bash + +✨ Displaying System Information + +╭─────────────────────────────────────────────────────────────────────────── System Information Panel ───────────────────────────────────────────────────────────────────────────╮ +│ ╭──────────────────┬───────────────────────────────────────────────────────────────────────╮ │ +│ │ System Parameter │ Parameter Value │ │ +│ ├──────────────────┼───────────────────────────────────────────────────────────────────────┤ │ +│ │ battery │ 77.25% battery life remaining, unknown seconds remaining │ │ +│ │ cpu │ x86_64 │ │ +│ │ cpucores │ Physical cores: 4, Logical cores: 8 │ │ +│ │ cpufrequencies │ Min: 400.0 Mhz, Max: 4400.0 Mhz │ │ +│ │ datetime │ 27/03/2025 17:41:34 │ │ +│ │ disk │ Using 43.44 GB of 97.87 GB │ │ +│ │ hostname │ cislaptop │ │ +│ │ memory │ Using 8.05 GB of 15.35 GB │ │ +│ │ platform │ Linux 6.11.0-21-generic │ │ +│ │ pythonversion │ 3.12.3 │ │ +│ │ runningprocesses │ 362 │ │ +│ │ swap │ Total: 4095 MB, Used: 0 MB, Free: 4095 MB │ │ +│ │ system │ Linux │ │ +│ │ systemload │ Average Load: 3.07, CPU Utilization: 3.90% │ │ +│ │ virtualenv │ /home/student/.cache/pypoetry/virtualenvs/systemsense-DC4abhLn-py3.12 │ │ +│ ╰──────────────────┴───────────────────────────────────────────────────────────────────────╯ │ +╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +🏁 Displaying Benchmark Results + +╭───────────────────────────────────────────────────────────────────────── Benchmark Information Panel ──────────────────────────────────────────────────────────────────────────╮ +│ ╭────────────────┬─────────────────────────────────────────────────────────────────╮ │ +│ │ Benchmark Name │ Benchmark Results (sec) │ │ +│ ├────────────────┼─────────────────────────────────────────────────────────────────┤ │ +│ │ addition │ [0.39699050600029295, 0.39876872999593616, 0.40132377600093605] │ │ +│ │ concatenation │ [1.9879290609969757, 1.9158207119980943, 1.9231829279960948] │ │ +│ │ exponentiation │ [1.9385030220000772, 1.9133422640006756, 2.0385779130010633] │ │ +│ │ multiplication │ [0.3794930040021427, 0.3551806879986543, 0.35516838500188896] │ │ +│ │ rangelist │ [0.10307988400018075, 0.0976890980018652, 0.0977334429990151] │ │ +│ ╰────────────────┴─────────────────────────────────────────────────────────────────╯ │ +╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +``` + +#### Run of Doubling Experiment + +```bash + +DLL Queue Implementation +╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ DLL Queue Doubling Experiment Results │ +│ ╭──────────┬──────────┬──────────┬──────────┬──────────┬──────────╮ │ +│ │ Size (n) │ enqueue │ dequeue │ peek │ concat │ iconcat │ │ +│ ├──────────┼──────────┼──────────┼──────────┼──────────┼──────────┤ │ +│ │ 100 │ 0.039566 │ 0.011458 │ 0.003488 │ 0.000625 │ 0.000858 │ │ +│ │ 200 │ 0.113858 │ 0.022507 │ 0.006425 │ 0.000403 │ 0.000311 │ │ +│ │ 400 │ 0.202240 │ 0.047851 │ 0.012279 │ 0.000379 │ 0.000280 │ │ +│ │ 800 │ 0.361492 │ 0.094825 │ 0.022960 │ 0.000330 │ 0.000355 │ │ +│ ╰──────────┴──────────┴──────────┴──────────┴──────────┴──────────╯ │ +╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +SLL Queue Implementation +╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ SLL Queue Doubling Experiment Results │ +│ ╭──────────┬──────────┬──────────┬──────────┬──────────┬──────────╮ │ +│ │ Size (n) │ enqueue │ dequeue │ peek │ concat │ iconcat │ │ +│ ├──────────┼──────────┼──────────┼──────────┼──────────┼──────────┤ │ +│ │ 100 │ 0.078195 │ 0.014763 │ 0.003961 │ 0.001582 │ 0.000946 │ │ +│ │ 200 │ 0.070928 │ 0.019607 │ 0.006181 │ 0.000761 │ 0.000489 │ │ +│ │ 400 │ 0.176146 │ 0.049838 │ 0.012285 │ 0.000710 │ 0.000490 │ │ +│ │ 800 │ 0.309921 │ 0.083965 │ 0.024419 │ 0.000687 │ 0.000442 │ │ +│ ╰──────────┴──────────┴──────────┴──────────┴──────────┴──────────╯ │ +╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +ARRAY Queue Implementation +╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ ARRAY Queue Doubling Experiment Results │ +│ ╭──────────┬──────────┬──────────┬──────────┬──────────┬──────────╮ │ +│ │ Size (n) │ enqueue │ dequeue │ peek │ concat │ iconcat │ │ +│ ├──────────┼──────────┼──────────┼──────────┼──────────┼──────────┤ │ +│ │ 100 │ 0.007520 │ 0.006178 │ 0.004555 │ 0.002887 │ 0.000509 │ │ +│ │ 200 │ 0.013283 │ 0.012204 │ 0.008401 │ 0.002614 │ 0.000355 │ │ +│ │ 400 │ 0.026229 │ 0.025153 │ 0.016454 │ 0.007943 │ 0.000658 │ │ +│ │ 800 │ 0.110293 │ 0.075074 │ 0.031935 │ 0.009347 │ 0.000694 │ │ +│ ╰──────────┴──────────┴──────────┴──────────┴──────────┴──────────╯ │ +╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +Plots saved to results directory + +``` + +#### Run of Performance Analysis + +```bash + + +DLL Queue Implementation +╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ DLL Queue Performance Analysis │ +│ ╭───────────┬───────────┬──────────┬───────────────────╮ │ +│ │ Operation │ Time (ms) │ Elements │ Time/Element (ms) │ │ +│ ├───────────┼───────────┼──────────┼───────────────────┤ │ +│ │ enqueue │ 0.477277 │ 1,000 │ 0.000477 │ │ +│ │ dequeue │ 0.117047 │ 500 │ 0.000234 │ │ +│ │ peek │ 0.032079 │ 333 │ 0.000096 │ │ +│ │ concat │ 0.000720 │ 100 │ 0.000007 │ │ +│ │ iconcat │ 0.000936 │ 100 │ 0.000009 │ │ +│ ╰───────────┴───────────┴──────────┴───────────────────╯ │ +╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +SLL Queue Implementation +╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ SLL Queue Performance Analysis │ +│ ╭───────────┬───────────┬──────────┬───────────────────╮ │ +│ │ Operation │ Time (ms) │ Elements │ Time/Element (ms) │ │ +│ ├───────────┼───────────┼──────────┼───────────────────┤ │ +│ │ enqueue │ 0.411052 │ 1,000 │ 0.000411 │ │ +│ │ dequeue │ 0.106158 │ 500 │ 0.000212 │ │ +│ │ peek │ 0.031625 │ 333 │ 0.000095 │ │ +│ │ concat │ 0.002308 │ 100 │ 0.000023 │ │ +│ │ iconcat │ 0.001400 │ 100 │ 0.000014 │ │ +│ ╰───────────┴───────────┴──────────┴───────────────────╯ │ +╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +ARRAY Queue Implementation +╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ ARRAY Queue Performance Analysis │ +│ ╭───────────┬───────────┬──────────┬───────────────────╮ │ +│ │ Operation │ Time (ms) │ Elements │ Time/Element (ms) │ │ +│ ├───────────┼───────────┼──────────┼───────────────────┤ │ +│ │ enqueue │ 0.115217 │ 1,000 │ 0.000115 │ │ +│ │ dequeue │ 0.069180 │ 500 │ 0.000138 │ │ +│ │ peek │ 0.044262 │ 333 │ 0.000133 │ │ +│ │ concat │ 0.012812 │ 100 │ 0.000128 │ │ +│ │ iconcat │ 0.001020 │ 100 │ 0.000010 │ │ +│ ╰───────────┴───────────┴──────────┴───────────────────╯ │ +╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +``` + +### Joseph Oforkansi + +#### Run of systemsense + +```wsl + +╭──────────────────────────────────── Displaying System Panel Information ────────────────────────────────────╮ +│ ┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ │ +│ ┃ System Parameter ┃ Parameter Value ┃ │ +│ ┣━━━━━━━━━━━━━━━━━━╋━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┫ │ +│ ┃ battery ┃ 49.48% battery life remaining, 3:34:11 seconds remaining ┃ │ +│ ┃ cpu ┃ x86_64 ┃ │ +│ ┃ cpucores ┃ Physical cores: 6, Logical cores: 12 ┃ │ +│ ┃ cpufrequencies ┃ Min: 0.0 Mhz, Max: 0.0 Mhz ┃ │ +│ ┃ datetime ┃ 2025-03-28 00:07:56 ┃ │ +│ ┃ disk ┃ Total: 1006.85 GB, Used: 4.94 GB, Free: 950.70 GB ┃ │ +│ ┃ hostname ┃ Ubasinachi ┃ │ +│ ┃ memory ┃ Total: 3.55 GB, Available: 2.97 GB, Used: 0.42 GB ┃ │ +│ ┃ platform ┃ Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.39 ┃ │ +│ ┃ pythonversion ┃ 3.12.3 ┃ │ +│ ┃ runningprocesses ┃ 30 ┃ │ +│ ┃ swap ┃ Total: 1.00 GB, Used: 0.00 GB, Free: 1.00 GB ┃ │ +│ ┃ system ┃ Linux ┃ │ +│ ┃ systemload ┃ (0.09033203125, 0.02294921875, 0.00537109375) ┃ │ +│ ┃ virtualenv ┃ /home/oforkansi/.cache/pypoetry/virtualenvs/systemsense-Rt5TvRuf-py3.12 ┃ │ +│ ┗━━━━━━━━━━━━━━━━━━┻━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ │ +╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + + +Displaying Benchmark Results + +╭────────────────────────────────── Displaying Benchmark Panel Information ───────────────────────────────────╮ +│ ┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ │ +│ ┃ Benchmark Name ┃ Benchmark Results (sec) ┃ │ +│ ┣━━━━━━━━━━━━━━━━╋━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┫ │ +│ ┃ addition ┃ [0.31629270799749065, 0.3047017440003401, 0.30586198799937847] ┃ │ +│ ┃ concatenation ┃ [1.252448921000905, 1.2106726849997358, 1.196216729000298] ┃ │ +│ ┃ exponentiation ┃ [3.4946560460011824, 2.8278707829995255, 2.6028115440021793] ┃ │ +│ ┃ multiplication ┃ [0.46731175999957486, 0.47331768200092483, 0.4569921219990647] ┃ │ +│ ┃ rangelist ┃ [0.18792873100028373, 0.1763109790008457, 0.17144616799851065] ┃ │ +│ ┗━━━━━━━━━━━━━━━━┻━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ │ +╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +``` + +#### Run of Doubling Experiment + +```wsl +DLL Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ DLL Queue Doubling Experiment Results │ +│ ╭──────────┬──────────┬──────────┬──────────┬──────────┬──────────╮ │ +│ │ Size (n) │ enqueue │ dequeue │ peek │ concat │ iconcat │ │ +│ ├──────────┼──────────┼──────────┼──────────┼──────────┼──────────┤ │ +│ │ 100 │ 0.078146 │ 0.021706 │ 0.005929 │ 0.001395 │ 0.001344 │ │ +│ │ 200 │ 0.204515 │ 0.048069 │ 0.011960 │ 0.000667 │ 0.000349 │ │ +│ │ 400 │ 0.248051 │ 0.056235 │ 0.015726 │ 0.000554 │ 0.000380 │ │ +│ │ 800 │ 0.438154 │ 0.106643 │ 0.029851 │ 0.000493 │ 0.000328 │ │ +│ ╰──────────┴──────────┴──────────┴──────────┴──────────┴──────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +SLL Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ SLL Queue Doubling Experiment Results │ +│ ╭──────────┬──────────┬──────────┬──────────┬──────────┬──────────╮ │ +│ │ Size (n) │ enqueue │ dequeue │ peek │ concat │ iconcat │ │ +│ ├──────────┼──────────┼──────────┼──────────┼──────────┼──────────┤ │ +│ │ 100 │ 0.064944 │ 0.017890 │ 0.007950 │ 0.003057 │ 0.001652 │ │ +│ │ 200 │ 0.216928 │ 0.023132 │ 0.008165 │ 0.001077 │ 0.000482 │ │ +│ │ 400 │ 0.203469 │ 0.047208 │ 0.015469 │ 0.000790 │ 0.000452 │ │ +│ │ 800 │ 0.389417 │ 0.094077 │ 0.078423 │ 0.001128 │ 0.000595 │ │ +│ ╰──────────┴──────────┴──────────┴──────────┴──────────┴──────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +ARRAY Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ ARRAY Queue Doubling Experiment Results │ +│ ╭──────────┬──────────┬──────────┬──────────┬──────────┬──────────╮ │ +│ │ Size (n) │ enqueue │ dequeue │ peek │ concat │ iconcat │ │ +│ ├──────────┼──────────┼──────────┼──────────┼──────────┼──────────┤ │ +│ │ 100 │ 0.014208 │ 0.011386 │ 0.009879 │ 0.006011 │ 0.000964 │ │ +│ │ 200 │ 0.024209 │ 0.053722 │ 0.016782 │ 0.005693 │ 0.000687 │ │ +│ │ 400 │ 0.031636 │ 0.028630 │ 0.063159 │ 0.008494 │ 0.000677 │ │ +│ │ 800 │ 0.102058 │ 0.097903 │ 0.088619 │ 0.022486 │ 0.001354 │ │ +│ ╰──────────┴──────────┴──────────┴──────────┴──────────┴──────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +``` + +#### Run of Performance Analysis + +```wsl +DLL Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ DLL Queue Performance Analysis │ +│ ╭───────────┬───────────┬──────────┬───────────────────╮ │ +│ │ Operation │ Time (ms) │ Elements │ Time/Element (ms) │ │ +│ ├───────────┼───────────┼──────────┼───────────────────┤ │ +│ │ enqueue │ 1.033531 │ 1,000 │ 0.001034 │ │ +│ │ dequeue │ 0.136059 │ 500 │ 0.000272 │ │ +│ │ peek │ 0.036996 │ 333 │ 0.000111 │ │ +│ │ concat │ 0.000893 │ 100 │ 0.000009 │ │ +│ │ iconcat │ 0.000914 │ 100 │ 0.000009 │ │ +│ ╰───────────┴───────────┴──────────┴───────────────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +SLL Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ SLL Queue Performance Analysis │ +│ ╭───────────┬───────────┬──────────┬───────────────────╮ │ +│ │ Operation │ Time (ms) │ Elements │ Time/Element (ms) │ │ +│ ├───────────┼───────────┼──────────┼───────────────────┤ │ +│ │ enqueue │ 1.117241 │ 1,000 │ 0.001117 │ │ +│ │ dequeue │ 0.232174 │ 500 │ 0.000464 │ │ +│ │ peek │ 0.051571 │ 333 │ 0.000155 │ │ +│ │ concat │ 0.002783 │ 100 │ 0.000028 │ │ +│ │ iconcat │ 0.001448 │ 100 │ 0.000014 │ │ +│ ╰───────────┴───────────┴──────────┴───────────────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ + +ARRAY Queue Implementation +╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ ARRAY Queue Performance Analysis │ +│ ╭───────────┬───────────┬──────────┬───────────────────╮ │ +│ │ Operation │ Time (ms) │ Elements │ Time/Element (ms) │ │ +│ ├───────────┼───────────┼──────────┼───────────────────┤ │ +│ │ enqueue │ 0.121895 │ 1,000 │ 0.000122 │ │ +│ │ dequeue │ 0.121803 │ 500 │ 0.000244 │ │ +│ │ peek │ 0.071548 │ 333 │ 0.000215 │ │ +│ │ concat │ 0.085331 │ 100 │ 0.000853 │ │ +│ │ iconcat │ 0.001982 │ 100 │ 0.000020 │ │ +│ ╰───────────┴───────────┴──────────┴───────────────────╯ │ +╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +``` + + +#### Run of Performance Analysis + +##### Key Finding + +- The Array implementation maintains its O(1) behavior for basic operations even with large data sizes +- The DLL and SLL implementations show clear O(n) behavior for enqueue operations with large data +- All implementations show O(1) behavior for peek operations, even with large data +- The Array implementation's concatenation operation shows clear O(n) behavior with large data +- The linked list implementations (DLL and SLL) maintain O(1) behavior for concatenation operations + +##### Final Recommendations: + +###### Use Array Queue + +When: +- Basic operations (enqueue, dequeue, peek) are the primary operations +- Memory efficiency is crucial +- Concatenation operations are rare +- Fixed or predictable size is expected + +###### Use DLL Queue + +When: +- Concatenation operations are frequent +- Bidirectional traversal is needed +- Dynamic size changes are common +- Memory overhead is not a concern + +###### Use SLL Queue + +When: +- Concatenation operations are frequent +- Memory efficiency is important +- Unidirectional traversal is sufficient +- Dynamic size changes are common +- Example: Task queue in a scheduler + +#### Conclusion + +The choice of queue implementation should be based on the specific requirements of the application: +- For applications prioritizing basic operations and memory efficiency, the Array implementation is the best choice +- For applications requiring frequent concatenation and dynamic size changes, either DLL or SLL implementation would be more suitable +- SLL provides a good balance between memory efficiency and functionality, while DLL offers the most flexibility at the cost of higher memory overhead +The performance analysis shows that there is no "one-size-fits-all" solution, and the optimal choice depends on the specific use case and requirements of the application. + + +#### Future Works + +- Analyzing performance under varying workloads and larger data sizes +- Measuring memory usage across different implementations +- Develops and experiment with hybrid implementations that combine strengths of different approaches \ No newline at end of file