ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference Article Swipe
Hyungjun Oh
,
KiHong Kim
,
Jaemin Kim
,
Sungkyun Kim
,
Junyeol Lee
,
Du-Seong Chang
,
Jiwon Seo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.07947
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.07947
This paper presents ExeGPT, a distributed system designed for constraint-aware LLM inference. ExeGPT finds and runs with an optimal execution schedule to maximize inference throughput while satisfying a given latency constraint. By leveraging the distribution of input and output sequences, it effectively allocates resources and determines optimal execution configurations, including batch sizes and partial tensor parallelism. We also introduce two scheduling strategies based on Round-Robin Allocation and Workload-Aware Allocation policies, suitable for different NLP workloads. We evaluate ExeGPT on six LLM instances of T5, OPT, and GPT-3 and five NLP tasks, each with four distinct latency constraints. Compared to FasterTransformer, ExeGPT achieves up to 15.2x improvements in throughput and 6x improvements in latency. Overall, ExeGPT achieves an average throughput gain of 2.9x across twenty evaluation scenarios. Moreover, when adapting to changing sequence distributions, the cost of adjusting the schedule in ExeGPT is reasonably modest. ExeGPT proves to be an effective solution for optimizing and executing LLM inference for diverse NLP workload and serving conditions.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.07947
- https://arxiv.org/pdf/2404.07947
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394782248
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394782248Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2404.07947Digital Object Identifier
- Title
-
ExeGPT: Constraint-Aware Resource Scheduling for LLM InferenceWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-15Full publication date if available
- Authors
-
Hyungjun Oh, KiHong Kim, Jaemin Kim, Sungkyun Kim, Junyeol Lee, Du-Seong Chang, Jiwon SeoList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.07947Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2404.07947Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2404.07947Direct OA link when available
- Concepts
-
Computer science, Workload, Inference, Latency (audio), Scheduling (production processes), Schedule, Constraint (computer-aided design), Throughput, Distributed computing, Mathematical optimization, Artificial intelligence, Operating system, Mathematics, Wireless, Telecommunications, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.allocates | 42 |
| abstract_inverted_index.different | 72 |
| abstract_inverted_index.effective | 149 |
| abstract_inverted_index.executing | 154 |
| abstract_inverted_index.execution | 19, 47 |
| abstract_inverted_index.including | 49 |
| abstract_inverted_index.inference | 23, 156 |
| abstract_inverted_index.instances | 81 |
| abstract_inverted_index.introduce | 58 |
| abstract_inverted_index.policies, | 69 |
| abstract_inverted_index.resources | 43 |
| abstract_inverted_index.Allocation | 65, 68 |
| abstract_inverted_index.determines | 45 |
| abstract_inverted_index.evaluation | 124 |
| abstract_inverted_index.inference. | 11 |
| abstract_inverted_index.leveraging | 32 |
| abstract_inverted_index.optimizing | 152 |
| abstract_inverted_index.reasonably | 142 |
| abstract_inverted_index.satisfying | 26 |
| abstract_inverted_index.scenarios. | 125 |
| abstract_inverted_index.scheduling | 60 |
| abstract_inverted_index.sequences, | 39 |
| abstract_inverted_index.strategies | 61 |
| abstract_inverted_index.throughput | 24, 107, 118 |
| abstract_inverted_index.workloads. | 74 |
| abstract_inverted_index.Round-Robin | 64 |
| abstract_inverted_index.conditions. | 163 |
| abstract_inverted_index.constraint. | 30 |
| abstract_inverted_index.distributed | 5 |
| abstract_inverted_index.effectively | 41 |
| abstract_inverted_index.constraints. | 96 |
| abstract_inverted_index.distribution | 34 |
| abstract_inverted_index.improvements | 105, 110 |
| abstract_inverted_index.parallelism. | 55 |
| abstract_inverted_index.Workload-Aware | 67 |
| abstract_inverted_index.distributions, | 132 |
| abstract_inverted_index.configurations, | 48 |
| abstract_inverted_index.constraint-aware | 9 |
| abstract_inverted_index.FasterTransformer, | 99 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.04787441 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |