Benchmarking Machine Learning Applications on Heterogeneous Architectures using the Reframe Framework

Core Concepts

The authors extend the Reframe testing framework to support Kubernetes as a backend scheduler, and utilize this framework to benchmark the performance of various machine learning applications, including ResNet-50, DeepCAM, and CosmoFlow, across a range of heterogeneous hardware platforms managed by EPCC.

Abstract

The authors present their work on extending the Reframe testing framework to support Kubernetes as a backend scheduler, enabling the benchmarking of machine learning applications on diverse hardware platforms managed by EPCC. The key highlights include: Integration of Kubernetes as a backend for the Reframe framework, allowing users to write regression tests and benchmarks for Kubernetes clusters. Demonstration and comparison of the performance of three machine learning benchmarks (ResNet-50, DeepCAM, and CosmoFlow) on various EPCC systems, including CPU, GPU, Graphcore Bow Pod64, and Cerebras CS-2. Discussion of the challenges encountered in porting and running these benchmarks on novel machine learning accelerators, such as the need for careful pipeline placement on Graphcore and the limited support for certain operations on the Cerebras system. Observation that the file system and I/O performance can have a significant impact on the overall training speed, sometimes overshadowing the gains from newer GPU hardware. The authors make the implementation of this work publicly available, allowing other HPC centers to utilize the Reframe framework for their own machine learning benchmarking and performance testing needs.

Stats

The authors provide the following performance metrics for the different hardware platforms: ResNet-50 on ImageNet1k (global batch size 32): ARCHER2 CPU (4 CPU): Compute Throughput 40.5 inputs/s, Effective Throughput 40.1 inputs/s ARCHER2 MI210 (4 GPU): Compute Throughput 293.2 inputs/s, Effective Throughput 226.6 inputs/s Cirrus V100 (4 GPU): Compute Throughput 138.0 inputs/s, Effective Throughput 134.3 inputs/s EIDF A100 (4 GPU): Compute Throughput 226.2 inputs/s, Effective Throughput 179.7 inputs/s Graphcore (8 IPU): Effective Throughput 255.6 inputs/s Cerebras CS-2 (1 WSE): Effective Throughput 452.0 inputs/s CosmoFlow (global batch size 32): ARCHER2 CPU (4 CPU): Compute Throughput 14.9 inputs/s, Effective Throughput 14.8 inputs/s ARCHER2 MI210 (4 GPU): Compute Throughput 479.9 inputs/s, Effective Throughput 72.5 inputs/s Cirrus V100 (4 GPU): Compute Throughput 112.2 inputs/s, Effective Throughput 78.1 inputs/s EIDF A100 (4 GPU): Compute Throughput 117.9 inputs/s, Effective Throughput 58.1 inputs/s Graphcore (8 IPU, half precision): Effective Throughput 14.5 inputs/s DeepCAM (global batch size 32): ARCHER2 CPU (8 CPU): Compute Throughput 6.1 inputs/s, Effective Throughput 6.1 inputs/s ARCHER2 MI210 (4 GPU): Compute Throughput 26.4 inputs/s, Effective Throughput 14.5 inputs/s Cirrus V100 (4 GPU): Compute Throughput 54.1 inputs/s, Effective Throughput 15.4 inputs/s EIDF A100 (4 GPU): Compute Throughput 101.7 inputs/s, Effective Throughput 13.7 inputs/s

Quotes

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Key Insights Distilled From

Benchmarking Machine Learning Applications on Heterogeneous Architecture using Reframe

by Christopher ... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10536.pdf

Deeper Inquiries

How can the Reframe framework be further extended to support other types of machine learning workloads, such as large language models, beyond the CNN models explored in this work

To extend the Reframe framework to support other types of machine learning workloads, such as large language models (LLMs), beyond the convolutional neural network (CNN) models explored in this work, several key strategies can be implemented: Custom Test Cases: Develop custom test cases tailored to the specific requirements of LLMs, considering their unique architecture and computational demands. This involves creating Python classes within Reframe that encapsulate the variables and parameters specific to LLMs. Parameterization: Implement parameterization within the test cases to allow for flexibility in testing different configurations and hyperparameters of LLM models. This enables researchers to easily modify and compare various settings for optimal performance. Integration with LLM Frameworks: Integrate Reframe with popular LLM frameworks such as BERT, GPT, or T5 to streamline the benchmarking process. This integration can provide standardized testing procedures and metrics for LLM models. Optimized Resource Allocation: Develop resource allocation strategies within Reframe to efficiently utilize the hardware resources available for LLM training. This includes optimizing the distribution of tasks across multiple nodes or accelerators for parallel processing. Performance Metrics: Define specific performance metrics relevant to LLMs, such as training time, convergence rate, and memory utilization, to evaluate and compare the efficiency of different hardware architectures for LLM workloads. By incorporating these strategies, Reframe can be extended to support a broader range of machine learning workloads, including LLMs, and provide a comprehensive framework for benchmarking and performance evaluation.

What strategies can be employed to mitigate the impact of file system and I/O performance on the overall training speed, especially for memory-intensive models like CosmoFlow and DeepCAM

Mitigating the impact of file system and I/O performance on the overall training speed, especially for memory-intensive models like CosmoFlow and DeepCAM, requires strategic approaches to optimize data loading and processing. Here are some strategies that can be employed: Data Preloading: Preload the dataset into the on-node storage or memory to reduce the data transfer time during training. By keeping the data closer to the processing units, the I/O bottleneck can be minimized, improving overall training speed. Data Pipelining: Implement data pipelining techniques to overlap data loading with computation, allowing for continuous processing without waiting for data transfers. This can significantly reduce the idle time of processing units and enhance training efficiency. Memory Management: Optimize memory usage by minimizing unnecessary data transfers and ensuring efficient utilization of available memory resources. This includes batch processing techniques and memory allocation strategies to handle large datasets effectively. Parallel Processing: Utilize parallel processing capabilities of the hardware architecture to distribute data loading and computation tasks across multiple cores or accelerators. This can improve throughput and reduce the impact of I/O latency on training speed. File System Optimization: Implement file system optimizations such as caching mechanisms, data compression techniques, and parallel I/O operations to enhance data access speed and reduce latency during training. By implementing these strategies, the impact of file system and I/O performance on training speed can be mitigated, leading to improved efficiency and faster convergence for memory-intensive machine learning models like CosmoFlow and DeepCAM.

Given the challenges encountered in porting the benchmarks to the Graphcore and Cerebras systems, how can the hardware vendors improve the support and ease of use for a wider range of machine learning models and applications

To address the challenges encountered in porting benchmarks to the Graphcore and Cerebras systems and improve support for a wider range of machine learning models and applications, hardware vendors can take the following steps: Enhanced Compiler Support: Provide comprehensive compiler support for popular machine learning frameworks like TensorFlow, PyTorch, and others to ensure seamless integration and compatibility with the hardware architecture. This includes optimizing compilers for specific model architectures and operations to improve performance. Model Compatibility: Expand the range of supported machine learning models and applications by enhancing compatibility and functionality for diverse model types, including CNNs, LLMs, and other specialized architectures. This involves addressing limitations in model support and ensuring robust execution across different hardware platforms. Documentation and Resources: Offer detailed documentation, tutorials, and resources for developers to facilitate the porting and optimization of machine learning models on the hardware systems. This includes providing best practices, code examples, and troubleshooting guides to streamline the development process. Collaboration with ML Community: Engage with the machine learning community to gather feedback, address specific use cases, and prioritize feature enhancements based on user requirements. Collaboration with researchers and developers can lead to tailored solutions and improved support for a wide range of applications. Performance Tuning Tools: Develop performance tuning tools and profiling capabilities that enable users to analyze and optimize the execution of machine learning models on the hardware. This includes tools for identifying bottlenecks, optimizing resource utilization, and fine-tuning parameters for enhanced performance. By implementing these strategies, hardware vendors can improve the support and ease of use for a wider range of machine learning models and applications, fostering innovation and efficiency in the development and deployment of AI solutions.