Practical Solutions for GPU-Accelerated Machine Learning Workloads
Addressing Performance Variability in Large-Scale Computing Clusters
Researchers at the University of Wisconsin-Madison have tackled the challenge of performance variability in GPU-accelerated machine learning (ML) workloads within large-scale computing clusters. The variability arises from hardware heterogeneity, software optimizations, and data-dependent ML algorithms, leading to inefficient resource utilization and unpredictable job completion times.
Current cluster schedulers struggle to effectively manage the performance variability inherent in ML workloads, often resulting in suboptimal resource allocation and inefficiencies. To address this, the researchers have introduced PAL (Performance-Aware Learning), a novel scheduler designed to embrace and mitigate the effects of performance variability in GPU-rich clusters.
PAL operates in two primary phases: performance profiling and scheduling decision-making. It collects detailed metrics on GPU utilization, memory bandwidth, and execution time for each job, as well as performance characteristics for individual nodes, allowing it to make informed scheduling decisions to improve job completion times, resource utilization, and overall cluster efficiency.
Experiments testing PAL against existing schedulers across various ML workloads, including image, language, and vision models, demonstrate that PAL significantly outperforms these schedulers, achieving a 42% improvement in job completion time, a 28% increase in cluster utilization, and a 47% reduction in makespan.
In conclusion, PAL represents a significant advancement in performance variability in GPU-accelerated ML workloads. By leveraging detailed performance profiling and adaptive scheduling, PAL effectively reduces job completion times, enhances resource utilization, and improves overall cluster performance.
Adopting AI Solutions for Business Optimization
If you are looking to evolve your company with AI and stay competitive, PAL offers a valuable solution for optimizing large-scale computing systems reliant on GPUs for ML and scientific applications.
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