AI Infrastructure Optimization
MLSynth: Towards Synthetic ML Traces
AI infrastructure continues to grow rapidly to meet the escalating demand for compute power required to train and inference increasingly capable models. This growth brings significant challenges in both the design and operation of ML pipelines. Exploring these challenges and evaluating potential solutions can be prohibitively expensive and time-consuming without effective simulation tools. This paper introduces MLSynth, a framework for synthesising ML workloads, which is essential for meaningful benchmarking of AI infrastructure. More specifically, MLSynth allows researchers to: (i) define a wide range of ML models with different parallelisation strategies, (ii) explore various sources of performance variability, and (iii) generate synthetic Chakra execution traces that can be used with existing simulation frameworks (e.g., ASTRA-Sim) to comprehensively model ML workloads.
Executive Impact & Strategic Value
MLSynth offers a transformative approach to AI infrastructure design by enabling accurate, reproducible, and cost-effective simulation of complex ML workloads. This capability significantly de-risks large-scale deployments, accelerates innovation, and uncovers hidden performance bottlenecks that would otherwise be prohibitively expensive to identify on physical test-beds.
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Accelerated Design Cycles
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Infrastructure Cost Reduction Potential
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Performance Gains Discovered
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
MLSynth provides a framework for synthesizing ML workloads, enabling comprehensive simulation and analysis of AI infrastructure. It addresses critical gaps in existing solutions by offering accurate, reproducible, and tunable workloads. Below are key insights:
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GPU Hours Wasted Due to Stragglers
MLSynth can model and analyze performance variability, revealing how stragglers waste significant GPU hours in large-scale ML deployments. This highlights the critical need for robust fault tolerance and scheduling in AI factories.
Enterprise Process Flow
ML Model Parametrisation
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Define Parallelisation
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Generate Chakra ET
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Simulate with ASTRA-Sim/ns-3
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Analyze Network Metrics
MLSynth provides an end-to-end workflow from high-level model definition to detailed network simulation metrics using Chakra Execution Traces. This allows for clear correlations between ML parameters and low-level network performance, crucial for informed design decisions.
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Compared to traditional methods, MLSynth offers superior accuracy, tunability, and cost-efficiency for ML infrastructure simulation, addressing key limitations of existing approaches.
Case Study: Replicating Megatron-LM Pipeline Parallelism
MLSynth was successfully used to reproduce a foundational experiment from the Megatron-LM paper, analyzing the scaling efficiency of 1F1B pipeline parallelism with varying batch sizes. This demonstrated MLSynth's ability to model complex ML scenarios accurately.
Key Outcome: MLSynth generated synthetic workloads that exhibited scaling behaviors consistent with physical test-beds, validating its accuracy for complex ML scenarios.
This case study demonstrates MLSynth's ability to accurately replicate complex ML training benchmarks, confirming its fidelity to real-world system behaviors and providing a reliable platform for future research.
Advanced ROI Calculator
Understand the potential ROI for your enterprise by leveraging synthetic ML traces for infrastructure optimization.
Projected Annual Savings
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Annual Hours Reclaimed
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Book a ConsultationYour Implementation Roadmap
A phased approach to integrating MLSynth into your AI infrastructure workflow for maximum impact and efficiency.
Phase 1: Workload Synthesis
Define ML model parameters and parallelisation strategies using MLSynth to generate Chakra Execution Traces tailored to your specific AI workloads.
Phase 2: Simulation & Analysis
Integrate the generated Chakra traces with full-stack simulators (e.g., ASTRA-Sim + ns-3) to accurately model computation, memory, and network interactions within your proposed infrastructure designs.
Phase 3: Optimization & Deployment
Analyze simulation results to identify performance bottlenecks, evaluate different network topologies and scheduling algorithms, and validate configurations before committing to costly physical deployments.
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