Harnessing GPU Performance Variability to Improve Scheduling of Machine Learning Workloads in GPU Clusters

Conceitos essenciais

Leveraging application-specific performance variability profiles and a novel placement policy called PAL, which co-optimizes for both performance variability and network locality, to significantly improve job completion times, cluster utilization, and makespan for machine learning workloads in GPU clusters.

Resumo

The content discusses the challenge of performance variability in GPU clusters running machine learning (ML) workloads, and proposes a novel scheduling approach to address this challenge.
Key highlights:

Large-scale GPU clusters exhibit significant performance variability, both within and across machines, due to factors like power management and thermal throttling. This variability can lead to resource under-utilization and load imbalance.
The authors leverage the insight that performance variability is application-specific - compute-intensive workloads exhibit higher variability than memory-bound ones.
They design a new cluster scheduler called PAL that co-optimizes for both performance variability and network locality when making GPU allocation decisions for multi-GPU jobs.
PAL uses application-specific variability profiles and a locality-variability matrix to balance these two factors and make efficient allocations.
Compared to state-of-the-art schedulers like Tiresias and Gandiva, PAL improves geomean job completion time by 42%, cluster utilization by 28%, and makespan by 47% across various ML workload traces.
The benefits of PAL are especially pronounced for workloads with a large proportion of multi-GPU jobs, which are becoming increasingly common as ML model sizes grow.

Estatísticas

The content does not provide specific numerical data points to support the key claims. However, it does mention the following statistics:

Compute-intensive workloads like training a ResNet-50 model exhibit 22% geomean variability, with a max of 3.5x.
Memory-intensive workloads like PageRank have only 1% geomean variability.

Citações

"Prior studies [12]–[14], [16]–[18] have found that large clusters with accelerators like general-purpose GPUs (GPGPUs) exhibit significant performance variability, both within and across machines."
"Our key insight to address this challenge is to characterize which applications are more likely to suffer from performance variability and take that into account while placing jobs on the cluster."
"Compared to Tiresias, PAL improves geomean 99th percentile JCT by 41%, average JCT by 42%, and makespan by 47%."

Principais Insights Extraídos De

PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters

by Rutwik Jain,... às arxiv.org 09-20-2024

https://arxiv.org/pdf/2408.11919.pdf

Perguntas Mais Profundas

What other types of workloads, beyond machine learning, could benefit from the variability-aware scheduling approach proposed in this work?

The variability-aware scheduling approach proposed in this work can be beneficial for a variety of workloads beyond machine learning (ML). These include:

High-Performance Computing (HPC) Applications: Many HPC applications, such as simulations in fluid dynamics, climate modeling, and molecular dynamics, often involve large-scale computations that can be sensitive to performance variability. The scheduling techniques that account for variability can help optimize resource allocation and improve overall execution times.

Data Analytics Workloads: Workloads that involve large-scale data processing, such as those found in big data analytics, can also benefit from variability-aware scheduling. These workloads often require significant computational resources and can experience performance variability due to data locality and resource contention.

Scientific Computing: Applications in scientific computing, such as those used in bioinformatics, physics simulations, and computational chemistry, can experience performance variability due to the heterogeneous nature of the underlying hardware. Scheduling policies that consider this variability can enhance performance and resource utilization.

Rendering and Graphics Processing: Workloads in rendering, such as those used in computer graphics and visual effects, can also benefit from variability-aware scheduling. These workloads often require significant GPU resources and can be sensitive to the performance characteristics of the underlying hardware.

Financial Modeling and Risk Analysis: Workloads in finance that involve complex simulations and risk assessments can also be impacted by performance variability. Variability-aware scheduling can help optimize the execution of these workloads, leading to faster results and better resource utilization.

By extending the variability-aware scheduling approach to these diverse workloads, the benefits of improved job performance and resource utilization can be realized across a broader range of applications.

How could the authors' approach be extended to handle dynamic changes in the performance variability characteristics of the cluster over time?

To extend the authors' approach to handle dynamic changes in the performance variability characteristics of the cluster over time, several strategies can be implemented:

Online Profiling and Adaptation: Implementing an online profiling mechanism that continuously monitors the performance of GPUs and other resources in real-time can help capture changes in performance variability. This data can be used to update the performance variability profiles dynamically, allowing the scheduling policies to adapt to the current state of the cluster.

Feedback Loops: Establishing feedback loops that analyze job performance metrics and resource utilization can help identify trends in performance variability. By integrating this feedback into the scheduling algorithms, the system can adjust its resource allocation strategies based on observed performance changes.

Machine Learning Techniques: Utilizing machine learning models to predict performance variability based on historical data can enhance the scheduling approach. These models can learn from past job executions and adapt the scheduling policies accordingly, improving the accuracy of resource allocation decisions.

Dynamic Resource Reallocation: Implementing mechanisms for dynamic resource reallocation can help address performance variability. If a GPU is identified as underperforming, jobs can be migrated to better-performing GPUs, ensuring that resource allocation remains optimal even as performance characteristics change.

Cluster State Awareness: Enhancing the scheduling algorithms to be more aware of the overall cluster state, including workload distribution and resource contention, can help in making more informed scheduling decisions. This awareness can lead to better handling of performance variability as workloads and resource availability fluctuate.

By incorporating these strategies, the authors' approach can become more robust and adaptable, effectively managing the dynamic nature of performance variability in GPU clusters.

Could the techniques used in PAL be applied to scheduling in other types of heterogeneous computing environments, such as CPU-GPU or CPU-FPGA clusters?

Yes, the techniques used in PAL can be effectively applied to scheduling in other types of heterogeneous computing environments, such as CPU-GPU or CPU-FPGA clusters. Here are several ways in which these techniques can be adapted:

Performance Variability Profiling: Similar to how PAL profiles GPU performance variability, the same profiling techniques can be applied to CPUs and FPGAs. By characterizing the performance variability of different processing units, the scheduling algorithms can make informed decisions based on the specific performance characteristics of each type of resource.

Application-Specific Profiles: The application-specific profiling approach used in PAL can be extended to include different types of workloads that run on CPUs, GPUs, and FPGAs. By understanding how different applications interact with various hardware components, the scheduler can optimize resource allocation based on the sensitivity of each application to performance variability.

Locality and Communication Overhead: The concepts of locality and communication overhead are relevant in CPU-GPU and CPU-FPGA environments as well. Scheduling policies can be designed to minimize inter-node communication costs while considering the performance variability of the resources involved, ensuring efficient execution of multi-component workloads.

Dynamic Resource Management: The dynamic resource management strategies employed in PAL can be adapted to heterogeneous environments. For instance, if a CPU is underperforming for a specific task, the scheduler can reallocate workloads to more capable CPUs or offload certain tasks to GPUs or FPGAs, optimizing overall performance.

Multi-Resource Scheduling: The techniques in PAL can be integrated into a multi-resource scheduling framework that considers the unique characteristics of CPUs, GPUs, and FPGAs. By developing a unified scheduling policy that accounts for the performance variability of all resource types, the system can achieve better overall performance and resource utilization.

By leveraging the principles of variability-aware scheduling from PAL, heterogeneous computing environments can benefit from improved performance, reduced execution times, and enhanced resource utilization across diverse workloads.