Current Launch Domain Resolution Process

Oct 13, 2025 by wbrum.org 10 views

Introduction

In distributed computing environments, optimizing task execution is crucial for achieving high performance and efficiency. One of the key challenges in task execution is determining the launch domain, which is the partitioning scheme used to divide the task among multiple processors. The launch domain resolution process involves filtering out stores that are not partitioned, collecting color shapes of the stores' partitions, and selecting a launch domain based on the collected information. However, this process can be complex and may not always produce optimal results. In this article, we will discuss the must-partition constraint, a new feature that helps optimize task execution in distributed environments.

Current Launch Domain Resolution Process

The current launch domain resolution process works as follows:

Filtering Out Unpartitioned Stores: The first step in the launch domain resolution process is to filter out all stores that are not partitioned, i.e., broadcasted. This is done to ensure that only stores that have been partitioned are considered for the launch domain.
Collecting Color Shapes: The next step is to collect all color shapes of the stores' partitions from the filtered list. Color shapes are used to represent the partitioning scheme of each store.
Singleton Color Shape: If the set of color shapes is singleton, the color shape is used as the launch domain. This is because a singleton color shape indicates that all stores have the same partitioning scheme.
Different Color Shapes with Same Volume: If the color shapes are different but have the same volume, a 1D launch domain of that volume is used. This is because the stores have different partitioning schemes, but the volume of each partition is the same.
Different Color Shapes with Different Volumes: If the color shapes have different volumes, the task is serialized. This is because the stores have different partitioning schemes and volumes, making it impossible to determine a launch domain.

Must-Partition Constraint

The must-partition constraint is a new feature that helps optimize task execution in distributed environments. A store/dimension that is marked with must-partition:

Does Not Get Preemptively Broadcasted: A store/dimension that is marked with must-partition does not get preemptively broadcasted. This means that the store/dimension is not automatically partitioned, and its existing partition is not considered when deciding the launch domain.
Existing Partition Not Considered: The existing partition of a store/dimension that is marked with must-partition is not considered when deciding the launch domain. This means that the store/dimension is always partitioned to align with another store/dimension in the same launch, even if that would result in empty sub-stores.
Repartitioned to Align with Another: A store/dimension that is marked with must-partition will be repartitioned to align with another store/dimension in the same launch, even if that would result in empty sub-stores. This means that the store/dimension is always partitioned to align with another store/dimension, even if it means creating empty sub-stores.
Higher Dimensionality: A store/dimension that is marked with must-partition will be repartitioned to align with another store/dimension in the same launch, even if its dimensionality is higher than that of the launch. This means that the store/dimension is always partitioned to align with another store/dimension, even if it means creating a higher-dimensional partition.

Relevance for Tasks with Independent Sets of Stores

The must-partition constraint is particularly relevant for tasks with independent sets of stores. For example, consider a task that involves a linear solve LHS, MxM, a linear solve RHS, MxN, and a linear solve x, MxN. In this case, the output and b would be marked as "must-partition", and that signals to Legate that it should ignore them when picking launch domains, and instead always partitions them to align with the partition decided based on other arguments.

No Other Arguments

If there are no other arguments, we should still partition, but just pick a 1D launch domain that has the same volume as the number of processors in scope. This means that the task will be partitioned, but the launch domain will be a 1D launch domain that has the same volume as the number of processors in scope.

Conclusion

In conclusion, the must-partition constraint is a new feature that helps optimize task execution in distributed environments. By marking a store/dimension as must-partition, we can ensure that it is always partitioned to align with another store/dimension in the same launch, even if that would result in empty sub-stores or a higher-dimensional partition. This feature is particularly relevant for tasks with independent sets of stores, and it can help improve the performance and efficiency of task execution in distributed environments.

Future Work

Future work on the must-partition constraint could involve exploring new ways to optimize task execution in distributed environments. For example, we could investigate the use of machine learning algorithms to predict the optimal launch domain for a given task. We could also explore the use of dynamic partitioning schemes that adapt to the changing needs of the task. By continuing to innovate and improve the must-partition constraint, we can help ensure that task execution in distributed environments remains efficient and effective.

Recommendations

Based on our analysis of the must-partition constraint, we recommend the following:

Use the Must-Partition Constraint: We recommend using the must-partition constraint to optimize task execution in distributed environments. By marking a store/dimension as must-partition, we can ensure that it is always partitioned to align with another store/dimension in the same launch.
Explore New Optimization Techniques: We recommend exploring new optimization techniques that can help improve the performance and efficiency of task execution in distributed environments. This could involve the use of machine learning algorithms or dynamic partitioning schemes.
Continuously Monitor and Evaluate: We recommend continuously monitoring and evaluating the performance and efficiency of task execution in distributed environments. This will help us identify areas for improvement and ensure that the must-partition constraint remains effective.

Conclusion

In conclusion, the must-partition constraint is a powerful tool for optimizing task execution in distributed environments. By marking a store/dimension as must-partition, we can ensure that it is always partitioned to align with another store/dimension in the same launch, even if that would result in empty sub-stores or a higher-dimensional partition. We recommend using the must-partition constraint to optimize task execution in distributed environments and exploring new optimization techniques to further improve performance and efficiency.

Introduction

The must-partition constraint is a new feature that helps optimize task execution in distributed environments. In this article, we will answer some of the most frequently asked questions about the must-partition constraint.

Q: What is the must-partition constraint?

A: The must-partition constraint is a feature that helps optimize task execution in distributed environments. It allows you to mark a store/dimension as must-partition, which means that it will always be partitioned to align with another store/dimension in the same launch, even if that would result in empty sub-stores or a higher-dimensional partition.

Q: Why do I need to use the must-partition constraint?

A: You need to use the must-partition constraint to optimize task execution in distributed environments. By marking a store/dimension as must-partition, you can ensure that it is always partitioned to align with another store/dimension in the same launch, which can improve the performance and efficiency of task execution.

Q: How do I use the must-partition constraint?

A: To use the must-partition constraint, you need to mark a store/dimension as must-partition. This can be done by adding a special attribute to the store/dimension, or by using a specific syntax in your code.

Q: What are the benefits of using the must-partition constraint?

A: The benefits of using the must-partition constraint include:

Improved performance: By ensuring that a store/dimension is always partitioned to align with another store/dimension in the same launch, you can improve the performance of task execution.
Increased efficiency: The must-partition constraint can help reduce the overhead of task execution by minimizing the number of partitions that need to be created.
Better scalability: The must-partition constraint can help improve the scalability of task execution by allowing you to easily add or remove stores/dimensions from the task.

Q: Are there any limitations to using the must-partition constraint?

A: Yes, there are some limitations to using the must-partition constraint. For example:

Complexity: The must-partition constraint can add complexity to your code, which can make it harder to maintain and debug.
Overhead: The must-partition constraint can introduce overhead, especially if you have a large number of stores/dimensions in your task.
Interoperability: The must-partition constraint may not be compatible with all task execution frameworks or libraries.

Q: Can I use the must-partition constraint with other optimization techniques?

A: Yes, you can use the must-partition constraint with other optimization techniques. In fact, combining the must-partition constraint with other optimization techniques can help improve the performance and efficiency of task execution even further.

Q: How do I troubleshoot issues with the must-partition constraint?

A: If you encounter issues with the must-partition constraint, you can try the following troubleshooting steps:

Check your code: Make sure that you have marked the store/dimension as must-partition correctly.
Check your task execution framework: Make sure that your task execution framework supports the must-partition constraint.
Check your system configuration: Make sure that your system configuration is compatible with the must-partition constraint.

Q: Can I use the must-partition constraint with different types of tasks?

A: Yes, you can use the must-partition constraint with different types of tasks. The must-partition constraint is a general-purpose optimization technique that can be applied to a wide range of tasks, including linear algebra, machine learning, and data processing tasks.

Q: Is the must-partition constraint compatible with different programming languages?

A: Yes, the must-partition constraint is compatible with different programming languages. You can use the must-partition constraint with languages such as C++, Python, Java, and C#.

Conclusion

In conclusion, the must-partition constraint is a powerful optimization technique that can help improve the performance and efficiency of task execution in distributed environments. By marking a store/dimension as must-partition, you can ensure that it is always partitioned to align with another store/dimension in the same launch, which can improve the performance and efficiency of task execution. We hope that this article has helped answer some of the most frequently asked questions about the must-partition constraint.