Boosting Performance: Grouped GEMMs in Triton Nvidia

Boosting Performance: Grouped GEMMs in Triton Nvidia

Table of Contents

1. Introduction
2. Overview of Group Jam
3. Motivating the Problem
4. The Single Stream Execution Model
5. The Concept of Grouping Gems
6. Advantages of Group Jam
7. Alternatives to Group Jam
8. Implementation of Group Jam in Triton
9. Performance Results
10. Cost and Optimization Considerations
11. Future Considerations and Improvements

Introduction

In this article, we will explore the concept of Group Jam and its potential to improve the performance and utilization of GPUs. Group Jam is a general mechanism that was initially introduced in Cutlass 2.8 and has since been adopted by many GPU users and use cases. The purpose of this article is to introduce this pattern to the Triton Community and provide an overview of its implementation.

Overview of Group Jam

Group Jam is a technique designed to address the problem of inefficient GPU utilization when running multiple gems. In this Context, a gem refers to a tiling decomposition of a computational task. The single stream execution model is commonly used to schedule and run these gems, but it often leads to underutilization of the GPU. Group Jam offers a solution by allowing gems to be grouped differently and assigning them to different Streaming Multiprocessors (SMs) for improved utilization.

Motivating the Problem

To understand the need for Group Jam, let's consider a Scenario where multiple gems need to be executed. Each gem is decomposed into tiles, and the single stream execution model is used to schedule and run them on different SMs. However, this approach results in idle SMs and underutilization of the GPU. A visual representation of this problem shows a significant amount of idle time in the system.

The Single Stream Execution Model

The single stream execution model works by sequentially picking a gem and scheduling its tiles on different SMs. This process continues until all the gems have been processed. However, due to quantization issues, idle SMs are often encountered, leading to suboptimal GPU utilization.

The Concept of Grouping Gems

To address the underutilization problem, Group Jam proposes a different approach to grouping and scheduling gems. Instead of executing gems separately, gems can be grouped together and assigned to different SMs. This grouping allows for better utilization of the GPU, reducing idle time and improving overall performance.

Advantages of Group Jam

One of the main advantages of Group Jam is that it allows for the saturation of GPUs and supports different gem shapes and element sizes. Additionally, Group Jam enables a single kernel launch instead of separate kernel launches, simplifying the execution process. Compared to alternatives like Bash Gem, Multi-streaming, and Horizontal Fusion, Group Jam offers improved performance and ease of implementation.

Implementation of Group Jam in Triton

The implementation of Group Jam in Triton involves a device-side static scheduler and a configurable number of virtual SMs. The scheduler assigns the gems to different SMs Based on a predetermined order of gem problems. This static scheduling approach reduces idle time and maximizes GPU utilization. An example of how the Group Jam API looks in Triton demonstrates its simplicity and ease of use.

Performance Results

Performance results from testing Group Jam Show significant speed improvements in certain scenarios. The experiments involved working with square matrices of varying sizes. In most cases, Group Jam demonstrated a substantial speedup and improved GPU utilization. However, there were some instances where slowdowns were observed, highlighting the need for further tuning and optimization.

Cost and Optimization Considerations

While Group Jam offers performance benefits, there are cost considerations to be aware of. Constructing group pointers and assigning pointers to arrays introduces additional overhead. However, variations in implementation can reduce this overhead, taking AdVantage of shared sizes and strides across multiple gems. Static planning and optimizing layouts can also improve performance and reduce kernel overhead.

Future Considerations and Improvements

The implementation and usage of Group Jam in Triton provide a promising solution for improving GPU utilization. However, there are several areas for further exploration and improvement. Static planning and optimization techniques could be utilized to reduce overhead and improve performance. Additionally, evaluating the impact of different gem sizes, shapes, and layouts on Group Jam's effectiveness would be valuable for understanding its applicability in various scenarios.

Highlights

• Group Jam is a general mechanism for improving GPU performance and utilization.
• It is implemented as a device-side static scheduler in Triton.
• Group Jam allows for the grouping of gems and better assignment to SMs for improved utilization.
• Performance results show substantial speed improvements in certain scenarios.
• Group Jam offers advantages over alternatives such as Bash Gem, Multi-streaming, and Horizontal Fusion.
• Cost and optimization considerations need to be taken into account for efficient implementation.
• Future improvements include static planning, layout optimization, and evaluating the impact of gem sizes and shapes on performance.

FAQ

Q: How does Group Jam compare to Cutlass in terms of performance?

A: A direct comparison between Group Jam and Cutlass performance has not been conducted. However, Group Jam can leverage some of the features available in Cutlass, making it a valuable technique to improve GPU utilization within Triton.

Q: Is there a trade-off in terms of cost when constructing group pointers?

A: Constructing group pointers can introduce additional overhead. However, optimizations can be applied to reduce this overhead, such as sharing sizes and strides across multiple gems or utilizing static planning techniques.

Q: Can Group Jam be used with batched gems?

A: Yes, Group Jam can be applied to batched gems as well. The scheduling and grouping principles can be extended to accommodate varying sizes of gems within a batch, improving utilization and performance.

Q: Is there a way to optimize the planning phase of Group Jam by knowing all the sizes ahead of time?

A: If all the sizes are known ahead of time, the planning phase of Group Jam can be further optimized. By eliminating the need for dynamic scheduling during kernel execution, overhead can be minimized, resulting in improved performance.

Q: Can Group Jam be used in scenarios where gems have drastically different layouts?

A: The layouts of gems can impact performance, especially when they are drastically different. However, Group Jam is compatible with tensor Core layouts, which can help mitigate the impact of layout variations on performance.