Enterprise AI Analysis
Dynamic Latency-Throughput Balancing in Distributed Large Model Inference with Interleaved Parallelism
This research introduces Liger+, a novel distributed large model inference system designed to dynamically balance latency and throughput in multi-GPU architectures. By implementing interleaved parallelism, which intelligently schedules computation and communication kernels across multiple requests, Liger+ effectively addresses the inherent trade-offs of traditional tensor and pipeline parallelism. It features a task-aware batch management module and a distributed runtime module with hybrid synchronization, resource contention anticipation, and runtime kernel decomposition. Evaluations demonstrate significant improvements in P90 latency reduction (up to 43.8%) and throughput enhancement (up to 1.63x) for both discriminative and generative tasks, showcasing its capacity for dynamic optimization.
Executive Impact Metrics
Liger+ significantly enhances inference performance across various tasks and hardware configurations, offering substantial improvements:
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P90 Latency Reduction (Discriminative)
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Throughput Increase (Discriminative)
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P90 Latency Reduction (Generative)
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Throughput Increase (Generative)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Interleaved Parallelism
Interleaved parallelism is a novel approach that dynamically balances latency and throughput by overlapping computation and communication kernels across multiple requests. Unlike traditional methods, it adapts to varying request rates, converging to tensor parallelism for low rates (optimizing latency) and pipeline parallelism for high rates (optimizing throughput). It intelligently utilizes idle periods of one resource type (e.g., computation) to execute tasks from another batch that primarily uses the other resource type (e.g., communication).
Batch Management
Liger+'s task-aware batch management module intelligently organizes requests into batches based on task type (discriminative or generative). For discriminative tasks, it uses fixed-size batching. For generative tasks, it employs a producer-consumer model to overlap prefill and decode phases, managing data dependencies and memory footprints effectively. This module constructs kernel launch function lists with metadata crucial for the distributed runtime's scheduling decisions.
Distributed Runtime
The distributed runtime module orchestrates computation and communication kernels across GPUs using a multi-stream scheduler. It incorporates a hybrid synchronization approach for precise kernel execution control, a resource contention anticipation strategy to mitigate performance degradation, and a runtime kernel decomposition technique to handle widely varied kernel durations. This ensures optimal overlap and efficient resource utilization, even in latency-critical scenarios.
43.8%
P90 Latency Reduction in Discriminative Tasks vs. Pipeline Parallelism
Enterprise Process Flow
New Request Arrival
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Batch Management (Task-Aware)
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Distributed Runtime (GPU Scheduling)
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Interleaved Parallelism Execution
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Dynamic Latency-Throughput Balancing
| Feature | Tensor Parallelism | Pipeline Parallelism |
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| Throughput |
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| Communication Overhead |
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GLM-130B Inference Optimization
When deploying GLM-130B on a 4 NVIDIA A100 GPUs node with PCIe interconnect, traditional tensor parallelism suffered from communication overhead, accounting for 47.1% of total execution time. Liger+ demonstrated an average 1.15x improvement in throughput and a 26.2% reduction in P90 latency.
Key Result: Liger+ significantly outperforms traditional methods in balancing latency and throughput for large generative models under challenging communication environments.
1.63x
Throughput Increase in Discriminative Tasks on A100 node vs. TP
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by optimizing large model inference with Liger+.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Journey to Optimized AI Inference
A structured approach to integrating Liger+ into your enterprise, ensuring seamless transition and maximum impact.
Phase 1: Initial Assessment & Setup
Evaluate current infrastructure, define performance benchmarks, and set up Liger+ prototype on a small scale. This involves hardware compatibility checks and initial model integration.
Phase 2: Performance Tuning & Customization
Analyze workload patterns, fine-tune Liger+'s scheduling parameters, and implement kernel decomposition strategies tailored to specific large models. Conduct iterative testing to optimize latency and throughput.
Phase 3: Integration & Scalability Testing
Integrate Liger+ into existing MLOps pipelines. Perform extensive scalability tests across multiple GPU nodes and diverse task types (discriminative/generative) to ensure robust performance under peak loads.
Phase 4: Monitoring & Continuous Optimization
Establish real-time monitoring of inference performance. Implement continuous integration and deployment (CI/CD) for model updates and ongoing optimization of Liger+'s configuration based on production data.
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