Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems (1708.05357v2)

Abstract: We propose a generic algorithmic building block to accelerate training of machine learning models on heterogeneous compute systems. Our scheme allows to efficiently employ compute accelerators such as GPUs and FPGAs for the training of large-scale machine learning models, when the training data exceeds their memory capacity. Also, it provides adaptivity to any system's memory hierarchy in terms of size and processing speed. Our technique is built upon novel theoretical insights regarding primal-dual coordinate methods, and uses duality gap information to dynamically decide which part of the data should be made available for fast processing. To illustrate the power of our approach we demonstrate its performance for training of generalized linear models on a large-scale dataset exceeding the memory size of a modern GPU, showing an order-of-magnitude speedup over existing approaches.

Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems

The paper "Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems" provides a comprehensive analysis and approach for utilizing heterogeneous computational resources, specifically targeting the efficient training of large-scale machine learning models. The authors propose an algorithmic framework that significantly enhances the use of compute accelerators, such as GPUs and FPGAs, by effectively managing their limited memory capacities when handling large datasets.

In the face of an increasingly heterogeneous computational environment characterized by differing degrees of parallelism, memory sizes, and communication bandwidths, the challenge lies in optimally distributing workload across diverse compute resources. This paper addresses this challenge by introducing a novel method that exploits primal-dual coordinate descent techniques combined with dynamic data selection based on duality gaps. The proposed method dynamically determines which part of the data should be prioritized for fast processing, thereby achieving greater computational efficiency.

Key Contributions

1. Theoretical Advancement: The paper extends the theoretical understanding of primal-dual block coordinate descent by incorporating approximate updates and leveraging coordinate-wise duality gaps for selection criteria. This extension facilitates a precise quantification of the enhanced convergence rate achieved over uniform sampling methods.
2. Adaptive Infrastructure: The authors develop an adaptable learning framework that can be seamlessly integrated within a compute system's memory hierarchy and resource constraints. The algorithm efficiently distributes data-intensive tasks to be handled by memory-rich, albeit computationally weaker units, while delegating compute-intensive tasks to powerful, memory-limited accelerators.
3. Empirical Evaluation: A significant empirical component of the paper demonstrates over a tenfold speedup in training times for generalized linear models when contrasted with existing approaches. This was illustrated using a dataset size exceeding the storage capacity of modern GPUs, underscoring the practical applicability of the proposed method.
4. Duality-Based Selection: Central to the proposed algorithm is a dynamic scheme that utilizes duality gap information for selecting active coordinate blocks, which improves workload distribution and reduces I/O operations over conventional batch processing methods.
5. Parallel Execution: The approach facilitates parallel processing between heterogeneous units—maximizing computational resources—while minimizing communication overhead.

Implications and Future Directions

This work holds significant practical implications for systems that integrate various computational accelerators. With the increasing complexity and heterogeneity of modern computing environments, the proposed method could serve as a robust component in the arsenal of tools for distributed machine learning, especially in settings where memory constraints pose a bottleneck.

From a theoretical perspective, the exploitation of duality gaps for dynamic data selection provides a promising direction for further research. Enhanced adaptivity and potential extensions to non-linear models or deep learning frameworks could further expand the utility of these methods across different domains.

Additionally, future research could explore the integration of this framework into broader distributed system architectures, where multiple nodes with variable computational capacities collaborate. This could also involve real-time adjustments and optimizations in response to varying data loads and system states.

In summary, this paper offers a critical advancement in the efficient training of machine learning models within heterogeneous environments. By successfully leveraging the distinctive strengths of varied computational units, it sets the stage for further exploration and innovation in distributed artificial intelligence systems.