MSDN Community Discussions - Discussion #103

Hi everyone,

I'm encountering some performance bottlenecks in my ASP.NET Core Web API. The requests for certain endpoints are taking longer than expected, and I'm struggling to pinpoint the exact cause. I've profiled the application using Visual Studio's diagnostic tools and found that a significant portion of the time is spent in CPU-bound operations, specifically within some custom data processing logic.

I've tried a few common optimizations:

Ensuring efficient LINQ queries.
Reducing unnecessary object allocations.
Using async/await correctly for I/O operations.

However, the CPU-bound part remains a challenge. I suspect there might be a more fundamental issue or a more advanced optimization technique I'm overlooking.

Here's a simplified snippet of the problematic code:


public async Task<IActionResult> GetDataAsync(int id)
{
    var rawData = await _dataService.GetRawDataAsync(id);
    if (rawData == null)
    {
        return NotFound();
    }

    var processedData = _processor.Process(rawData); // This is the CPU-intensive part

    return Ok(processedData);
}

// In _processor.Process:
// Complex calculations, heavy data transformations
public List<ProcessedItem> Process(RawData data)
{
    // ... thousands of iterations, complex logic ...
    var results = new List<ProcessedItem>();
    for (int i = 0; i < data.Items.Count; i++)
    {
        // Simulate complex processing
        var processedItem = new ProcessedItem
        {
            Id = data.Items[i].Id * 2,
            Name = data.Items[i].Name.ToUpper() + "_PROCESSED",
            Value = data.Items[i].Value + CalculateOffset(i)
        };
        results.Add(processedItem);
    }
    return results;
}

private int CalculateOffset(int index)
{
    int offset = 0;
    for (int j = 0; j < 100; j++) // Simulate more work
    {
        offset += (index % (j + 1)) * j;
    }
    return offset;
}

Has anyone faced similar issues with CPU-bound operations in .NET Core Web APIs? What strategies did you employ? Any insights into profiling or optimizing such code would be greatly appreciated.

Replies

Reply from MikeTech

Hi Jane,

Your code snippet looks reasonable for I/O, but that `_processor.Process` method is indeed where the CPU load is. If that `for` loop and `CalculateOffset` are truly doing a lot of work, here are a few ideas:

Parallelization: Can you parallelize the loop? .NET's TPL (Task Parallel Library) can help. Consider using `Parallel.For` or `Parallel.ForEach` if the operations within the loop are independent.
Span<T> and ValueTypes: For performance-critical code, especially with large collections, using `Span<T>` and value types can significantly reduce memory allocations and improve cache locality.
SIMD (Single Instruction, Multiple Data): If your calculations are amenable to it (e.g., vector operations on numbers), you could explore using `System.Numerics.Vectors` for hardware-accelerated parallel processing on multiple data points simultaneously.
Benchmarking: Before and after any changes, use a micro-benchmarking tool like BenchmarkDotNet to accurately measure the impact of your optimizations.

Could you provide more details about what `Process` and `CalculateOffset` actually do? The devil is often in the details.

Reply from SamCoder

Jane,

I've found that often the "custom data processing logic" can be optimized by rethinking the algorithm itself. Sometimes a different approach (e.g., sorting data first, using a different data structure, or a divide-and-conquer strategy) can yield much better results than just micro-optimizing the current implementation.

Also, consider the context of your web API. Is this processing always needed for every request? Could some of it be done asynchronously in the background if the user doesn't need the result immediately? Or perhaps cached?

For profiling, besides VS tools, have you looked at tools like PerfView? It can give very deep insights into CPU usage, GC, and more.

Replies

Add a Reply