Funneling Blocking Operations through a Common Interface

Oct 13, 2025 by pynkocean.com 109 views

Introduction

In the context of parallel computing, the use of par_nosync is a common technique to avoid synchronization between threads. However, when using this policy, it is essential to ensure that the algorithm will not block, as this can lead to unexpected behavior and performance issues. In this article, we will discuss the current state of affairs and propose solutions to improve the diagnostic capabilities of the Thrust library when using par_nosync.

Current State of Affairs

The Thrust library provides a par_nosync policy that allows users to execute algorithms in parallel without synchronization. However, this policy does not guarantee that the algorithm will not block. In fact, some algorithms may block even when using par_nosync. This can lead to unexpected behavior and performance issues.

Options for Improvement

To improve the diagnostic capabilities of the Thrust library, we propose the following options:

Option 1: Add a new nosync policy type that will fail at compile time if blocking would happen

One possible solution is to add a new nosync policy type that will fail at compile time if blocking would happen. This would ensure that users are aware of the potential blocking behavior of an algorithm and can take necessary precautions.

Option 2: Add a macro that would make the existing `par_nosync` fail at compile time if blocking happens

Another possible solution is to add a macro that would make the existing par_nosync fail at compile time if blocking happens. This would provide a way for users to opt-in to the new behavior without introducing new API names.

Option 3: Any of the above options, but with compile time warnings instead of failures

A third option is to implement any of the above options, but with compile-time warnings instead of failures. This would provide a way for users to be aware of potential blocking behavior without introducing new API names.

Funneling Blocking Operations through a Common Interface

Current State of Blocking Operations

We already do this for some obvious operations like cuda(Stream|Device)Synchronize. However, we don't do this for cudaFree, which always blocks. We also don't do this for cudaMalloc, which rarely blocks, but can under some circumstances on multi-GPU systems which cross-GPU allocation visibility enabled.

Comprehensive List of Blocking Operations

To improve our documentation and provide a comprehensive list of blocking operations, we should speak with someone from the CUDA driver team and get a list of all CUDA runtime interfaces that block under subtle conditions.

Does the CUDA Driver/Runtime Accurately Document Which Interfaces Can Block?

Unfortunately, the CUDA driver/runtime does not accurately document which interfaces can block. This makes it difficult for users to understand the potential blocking behavior of an algorithm and take necessary precautions.

Conclusion

In conclusion, the current state of affairs with par_nosync is that it does not guarantee that the algorithm will not block. To improve the diagnostic capabilities of the Thrust library, we propose the following options: add a new nosync policy type that will fail at compile time if blocking would happen, add a macro that would make the existing par_nosync fail at compile time if blocking happens, or any of the above options, but with compile-time warnings instead of failures. We also need to funnel all blocking operations through a common interface and document which algorithms can block.

Future Work

To implement any of the above options, we need to funnel all blocking operations through a common interface. This will allow us to document which algorithms can block and provide a way to detect blocking behavior at compile time. We should also speak with someone from the CUDA driver team and get a comprehensive list of blocking operations.

Recommendations

Based on our analysis, we recommend the following:

Add a new nosync policy type that will fail at compile time if blocking would happen.
Add a macro that would make the existing par_nosync fail at compile time if blocking happens.
Funnel all blocking operations through a common interface.
Document which algorithms can block.

Q: What is the current state of affairs with `par_nosync` in the Thrust library?

A: The current state of affairs with par_nosync is that it does not guarantee that the algorithm will not block. This can lead to unexpected behavior and performance issues.

Q: Why is it essential to ensure that the algorithm will not block when using `par_nosync`?

A: It is essential to ensure that the algorithm will not block when using par_nosync because blocking can lead to unexpected behavior and performance issues. This can cause the program to hang or crash, and can also lead to performance issues due to the blocking of threads.

Q: What are the options for improving the diagnostic capabilities of the Thrust library when using `par_nosync`?

A: The options for improving the diagnostic capabilities of the Thrust library when using par_nosync are:

Add a new nosync policy type that will fail at compile time if blocking would happen.
Add a macro that would make the existing par_nosync fail at compile time if blocking happens.
Any of the above options, but with compile-time warnings instead of failures.

Q: Why is it necessary to funnel all blocking operations through a common interface?

A: It is necessary to funnel all blocking operations through a common interface because this will allow us to document which algorithms can block and provide a way to detect blocking behavior at compile time.

Q: Does the CUDA driver/runtime accurately document which interfaces can block?

A: Unfortunately, the CUDA driver/runtime does not accurately document which interfaces can block. This makes it difficult for users to understand the potential blocking behavior of an algorithm and take necessary precautions.

Q: What is the recommended approach for improving the diagnostic capabilities of the Thrust library when using `par_nosync`?

A: The recommended approach for improving the diagnostic capabilities of the Thrust library when using par_nosync is to add a new nosync policy type that will fail at compile time if blocking would happen, add a macro that would make the existing par_nosync fail at compile time if blocking happens, or any of the above options, but with compile-time warnings instead of failures.

Q: What are the benefits of funneling all blocking operations through a common interface?

A: The benefits of funneling all blocking operations through a common interface are:

We can document which algorithms can block and provide a way to detect blocking behavior at compile time.
We can improve the diagnostic capabilities of the Thrust library when using par_nosync.

Q: What is the next step in improving the diagnostic capabilities of the Thrust library when using `par_nosync`?

A: The next step in improving the diagnostic capabilities of the Thrust library when using par_nosync is to funnel all blocking operations through a common interface and document which algorithms can block.

Q: How can users contribute to improving the diagnostic capabilities of the Thrust library when using `par_nosync`?

A: Users can contribute to improving the diagnostic capabilities of the Thrust library when using par_nosync by:

Providing feedback on the current state of affairs with par_nosync.
Suggesting new features or approaches for improving the diagnostic capabilities of the Thrust library when using par_nosync.
Helping to implement the recommended approach for improving the diagnostic capabilities of the Thrust library when using par_nosync.

Q: What is the expected outcome of improving the diagnostic capabilities of the Thrust library when using `par_nosync`?

A: The expected outcome of improving the diagnostic capabilities of the Thrust library when using par_nosync is to provide a more accurate understanding of the potential blocking behavior of an algorithm and to improve the performance and reliability of the Thrust library.