TimeRL: Efficient Deep Reinforcement Learning with Polyhedral Dependence Graphs (2501.05408v1)

Abstract: Modern deep learning (DL) workloads increasingly use complex deep reinforcement learning (DRL) algorithms that generate training data within the learning loop. This results in programs with several nested loops and dynamic data dependencies between tensors. While DL systems with eager execution support such dynamism, they lack the optimizations and smart scheduling of graph-based execution. Graph-based execution, however, cannot express dynamic tensor shapes, instead requiring the use of multiple static subgraphs. Either execution model for DRL thus leads to redundant computation, reduced parallelism, and less efficient memory management. We describe TimeRL, a system for executing dynamic DRL programs that combines the dynamism of eager execution with the whole-program optimizations and scheduling of graph-based execution. TimeRL achieves this by introducing the declarative programming model of recurrent tensors, which allows users to define dynamic dependencies as intuitive recurrence equations. TimeRL translates recurrent tensors into a polyhedral dependence graph (PDG) with dynamic dependencies as symbolic expressions. Through simple PDG transformations, TimeRL applies whole-program optimizations, such as automatic vectorization, incrementalization, and operator fusion. The PDG also allows for the computation of an efficient program-wide execution schedule, which decides on buffer deallocations, buffer donations, and GPU/CPU memory swapping. We show that TimeRL executes current DRL algorithms up to 47$\times$ faster than existing DRL systems, while using 16$\times$ less GPU peak memory.

• The paper introduces TimeRL, which integrates recurrent tensors and polyhedral dependence graphs to optimize deep reinforcement learning workflows.
• The methodology employs advanced transformations like vectorization, incrementalization, and operator fusion to minimize redundant computations.
• Key numerical results highlight up to 47x faster execution and 16x lower peak GPU memory usage, significantly enhancing resource management.

Insightful Overview of "TimeRL: Efficient Deep Reinforcement Learning with Polyhedral Dependence Graphs"

The paper by Silvestre and Pietzuch introduces "TimeRL," a system designed to optimize deep reinforcement learning (DRL) workloads using the theoretical framework of polyhedral dependence graphs (PDGs). As DRL algorithms increasingly incorporate complex computational dynamics due to interactions with simulated environments, the TimeRL system addresses inefficiencies that arise from current deep learning (DL) execution models. Both eager execution and graph-based execution models present limitations in optimizing DRL processes characterized by deeply nested loops and dynamic data dependencies. TimeRL combines the adaptability of eager execution with the optimization capabilities of graph-based models, resulting in significant improvements in computational efficiency and resource management.

Key Contributions

TimeRL innovates on several fronts, particularly through:

1. Recurrent Tensors (RTs): This programming model allows users to define dynamic dependencies in DRL algorithms as intuitive recurrence relations. RTs can be indexed symbolically, encoding dynamic control-flow and dependencies naturally and allowing typical transformations required for execution without manual slicing or trimming. RTs effectively separate the algorithm's computational semantics from its execution strategy, facilitating clean and scalable DRL implementations.
2. Polyhedral Dependence Graphs (PDGs): PDGs serve as the backbone for TimeRL's optimization process. These graphs represent entire DRL programs, capturing dynamic dependencies as symbolic expressions. Through transformations such as vectorization, incrementalization, and operator fusion, TimeRL systematically reduces redundant computations, enhances parallel execution opportunities, and optimizes memory usage.
3. Execution Scheduling: Leveraging the polyhedral model of computation, TimeRL analyzes dependencies and schedules optimizations across the entire program. It automates the allocation and deallocation of computational resources, as well as the management of memory transfers between CPU and GPU. The scheduling process ensures efficient resource usage, reducing GPU peak memory usage significantly.

Strong Numerical Results

The paper demonstrates TimeRL's capability to execute current DRL algorithm implementations up to 47 times faster than existing frameworks, with a peak GPU memory reduction of up to 16 times. Such notable performance metrics arise from its integrated and holistic approach to program optimization and scheduling, which reduces computational latency and resource overheads that traditionally hamper DRL efficiency.

Implications and Future Directions

TimeRL's approach represents a significant advancement in optimizing dynamic, complex DL workloads, setting a precedent for handling other dynamic computational patterns prevalent in AI applications, such as recurrent and transformer-based models. As the need for scaling DRL computations continues, TimeRL's framework could inspire further innovations in both runtime systems and intermediate representations, potentially enabling seamless integration with emerging AI models like RLHF-tuned LLMs.

Moreover, by supporting efficient algorithm-specific execution adaptations, TimeRL could influence the design of next-generation compilers and DL systems. The principles employed by TimeRL have the potential to generalize dynamic computation beyond DRL, impacting other domains with similar computational characteristics.

Conclusion

The development of TimeRL marks a pivotal step forward in optimizing DRL program execution. Through recurrent tensors and polyhedral dependence graphs, TimeRL manages to combine the strengths of eager execution dynamism with the optimization prowess of static graph-based execution. The results of the paper indicate not only substantial improvements in execution efficiency and memory management for DRL workloads but also provide a framework for further enhancement of dynamic DL systems in the future.