QDP DataLoader: Benchmarking Vs Qiskit, PennyLane, CUDA-Q

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QDP DataLoader: Benchmarking vs Qiskit, PennyLane, CUDA-Q

Hey guys! Let's dive into a roadmap for benchmarking our dataloader_throughput example in qdp-core. We're aiming to see how it stacks up against other frameworks like Qiskit, PennyLane, and CUDA-Q. Buckle up; it's gonna be a fun ride!

Summary

We've rolled out a dataloader_throughput example in qdp-core that's all about measuring the end-to-end encoding throughput for a QML-style DataLoader pipeline on QDP. It’s pretty neat! Now, to take things up a notch, we need to run a small benchmark.

The main goal? Compare this DataLoader throughput against equivalent workloads in other frameworks. Think Qiskit, PennyLane, CUDA-Q – the usual suspects. We'll pit them against each other on the same GPU and configuration to keep things fair and square.

Why This Matters

Benchmarking isn't just about numbers; it's about understanding where we shine and where we can improve. By comparing QDP's DataLoader with other frameworks, we can:

  • Identify Performance Bottlenecks: Pinpoint areas in our pipeline that might be slowing us down.
  • Highlight Strengths: Showcase what QDP does exceptionally well.
  • Inform Optimization: Use the data to guide future optimizations and improvements.
  • Provide a Baseline: Establish a clear performance baseline for future QDP development.

Key Considerations for Benchmarking

To ensure our benchmark is meaningful, we need to consider a few key factors:

  • Hardware Consistency: Running all tests on the same GPU and configuration is crucial. This eliminates hardware variability as a factor.
  • Software Versions: Clearly document the versions of QDP, Qiskit, PennyLane, CUDA-Q, and any other relevant libraries.
  • Workload Definition: The workload (batch size, vector length, #qubits, #batches) must be precisely defined and consistent across all frameworks.
  • Measurement Methodology: Use a consistent method for measuring throughput (e.g., vectors/second) and account for any warm-up or cool-down periods.

Goals

Let's break down what we want to achieve:

  1. Define a Canonical Workload: We need a simple, standard workload. Think about setting parameters like batch size, vector length, the number of qubits, and the number of batches. This will be our yardstick.
  2. Run the DataLoader/Encode Loop: We'll run the DataLoader/encode loop in:
    • QDP (using our dataloader_throughput example).
    • One or two competitor frameworks. Qiskit, PennyLane, or CUDA-Q are good starting points.
  3. Report Throughput: We're looking for throughput in vectors/sec for each framework. The settings need to be comparable, so it’s an apples-to-apples comparison.

Diving Deeper into the Goals

Let's expand on each of these goals to provide a clearer picture.

Defining a Canonical Workload

Creating a standardized workload is essential for fair comparisons. Here’s what we need to nail down:

  • Batch Size: The number of data samples processed in one iteration. A common starting point might be 32, 64, or 128.
  • Vector Length: The size of the input vectors. This determines the complexity of the encoding process. Common vector lengths could be 1024, 2048, or 4096.
  • Number of Qubits: The number of qubits used in the quantum circuit. This is a critical parameter for quantum simulations. Start with a reasonable number like 10, 12, or 14 qubits.
  • Number of Batches: The total number of batches to process. This ensures that the benchmark runs for a sufficient duration to obtain stable throughput measurements. Aim for at least 100 batches.

We should document these parameters clearly and provide rationale for the chosen values. A well-defined workload ensures reproducibility and comparability across different frameworks.

Running the DataLoader/Encode Loop

Once we have our workload defined, we need to implement the DataLoader and encoding loop in each framework. Here are some considerations:

  • QDP: Leverage the existing dataloader_throughput example. Ensure it’s configured to use the defined workload parameters.
  • Competitor Frameworks: Write minimal scripts that replicate the same DataLoader and encoding process. Focus on achieving functional equivalence rather than micro-optimizations.
  • Framework-Specific Optimizations: While aiming for functional equivalence, it’s acceptable to use framework-specific optimizations that are considered best practices. Just make sure to document these clearly.

For example, in Qiskit, we might use the Sampler primitive for efficient execution of quantum circuits. In PennyLane, we might explore different device options and optimization techniques.

Reporting Throughput

Accurate and consistent reporting of throughput is essential for drawing meaningful conclusions. Here’s how we should approach it:

  • Units: Report throughput in vectors/second. This provides a clear and intuitive measure of performance.
  • Averaging: Run each benchmark multiple times (e.g., 5-10 runs) and report the average throughput. This helps to reduce the impact of random variations.
  • Error Bars: Include error bars (e.g., standard deviation) to indicate the variability in the measurements.
  • Warm-up and Cool-down: Exclude the first few iterations (warm-up) and the last few iterations (cool-down) from the throughput calculation. This helps to avoid transient effects.

In addition to the throughput numbers, we should also report key hardware and software configurations, such as GPU model, driver version, and framework versions.

Deliverables

Here's what we need to deliver to call this a success:

  1. Benchmark Script for QDP: A script or example that runs the QDP DataLoader test in “benchmark mode.” This should be easy to execute and configure.
  2. Competitor Scripts: Minimal scripts for competitor frameworks that implement the same workload and log throughput. Keep them simple and focused.
  3. Markdown Summary: A short markdown summary (table of results + a brief discussion) under docs/ or docs/benchmarks/. This will be our final report.

Expanding on the Deliverables

Let’s detail what each deliverable should contain.

Benchmark Script for QDP

The benchmark script for QDP should be a modified version of the existing dataloader_throughput example. Here are some key features to include:

  • Command-Line Arguments: Allow users to specify the workload parameters (batch size, vector length, #qubits, #batches) via command-line arguments.
  • Benchmark Mode: Add a “benchmark mode” that runs the DataLoader and encoding loop multiple times and calculates the average throughput.
  • Logging: Implement clear logging of the throughput measurements, including the average, standard deviation, and hardware/software configurations.
  • Configuration File Support: Optionally, allow users to specify the workload parameters via a configuration file. This can be useful for more complex scenarios.

Ideally, the benchmark script should be easy to use and require minimal setup.

Competitor Scripts

The competitor scripts should be as simple as possible while still accurately replicating the QDP DataLoader workload. Here are some guidelines:

  • Focus on Functional Equivalence: Prioritize achieving the same functionality as the QDP DataLoader, rather than trying to micro-optimize the code.
  • Minimal Dependencies: Minimize the number of external dependencies to reduce the risk of conflicts and simplify the setup process.
  • Clear Documentation: Provide clear documentation on how to install the required dependencies and run the script.
  • Logging: Implement clear logging of the throughput measurements and hardware/software configurations.

For example, a competitor script might use a simple loop to generate random input vectors and encode them using the framework’s built-in functions.

Markdown Summary

The markdown summary should be a concise and informative report that summarizes the benchmark results. Here’s a suggested structure:

  • Introduction: Provide a brief overview of the benchmark and its goals.
  • Workload Definition: Clearly define the workload parameters used in the benchmark.
  • Hardware and Software Configurations: List the hardware and software configurations used for each framework.
  • Results Table: Present the throughput results in a table, including the average throughput and error bars for each framework.
  • Discussion: Discuss the results, highlighting any significant differences in performance and providing possible explanations.
  • Conclusion: Summarize the key findings and suggest areas for future investigation.

The markdown summary should be written in a clear and concise style, using tables and figures to effectively communicate the results.

Conclusion

So, there you have it! Our roadmap for benchmarking the QDP DataLoader against the competition. By defining a clear workload, running the tests on comparable settings, and summarizing the results in a markdown document, we'll gain valuable insights into QDP's performance. Let's get this show on the road and see how QDP stacks up against the big players!