Matches in SemOpenAlex for { <https://semopenalex.org/work/W4315567908> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W4315567908 endingPage "87" @default.
- W4315567908 startingPage "68" @default.
- W4315567908 abstract "Reinforcement learning (RL) is exploited for cluster scheduling in the field of high-performance computing (HPC). One of the key challenges for RL driven scheduling is state representation for RL agent (i.e., capturing essential features of dynamic scheduling environment for decision making). Existing state encoding approaches either lack critical scheduling information or suffer from poor scalability. In this study, we present SEM (Scalable and Efficient encoding Model) for general RL driven scheduling in HPC. It captures system resource and waiting job state, both being critical information for scheduling. It encodes these pieces of information into a fixed-sized vector as an input to the agent. A typical agent is built on deep neural network, and its training/inference cost grows exponentially with the size of its input. Production HPC systems contain a large number of computer nodes. As such, a direct encoding of each of the system resources would lead to poor scalability of the RL agent. SEM uses two techniques to transform the system resource state into a small-sized vector, hence being capable of representing a large number of system resources in a vector of 100–200. Our trace-based simulations demonstrate that compared to the existing state encoding methods, SEM can achieve 9X training speedup and 6X inference speedup while maintaining comparable scheduling performance." @default.
- W4315567908 created "2023-01-11" @default.
- W4315567908 creator A5010928934 @default.
- W4315567908 creator A5060400468 @default.
- W4315567908 creator A5065154024 @default.
- W4315567908 creator A5070703870 @default.
- W4315567908 date "2023-01-01" @default.
- W4315567908 modified "2023-09-27" @default.
- W4315567908 title "Encoding for Reinforcement Learning Driven Scheduling" @default.
- W4315567908 cites W2010041938 @default.
- W4315567908 cites W2107726111 @default.
- W4315567908 cites W2109255472 @default.
- W4315567908 cites W2112168774 @default.
- W4315567908 cites W2112219590 @default.
- W4315567908 cites W2120615054 @default.
- W4315567908 cites W2125998068 @default.
- W4315567908 cites W2147598193 @default.
- W4315567908 cites W2546571074 @default.
- W4315567908 cites W2792423399 @default.
- W4315567908 cites W2968986602 @default.
- W4315567908 cites W3117900013 @default.
- W4315567908 cites W3121689374 @default.
- W4315567908 cites W3129362935 @default.
- W4315567908 cites W3176930059 @default.
- W4315567908 doi "https://doi.org/10.1007/978-3-031-22698-4_4" @default.
- W4315567908 hasPublicationYear "2023" @default.
- W4315567908 type Work @default.
- W4315567908 citedByCount "0" @default.
- W4315567908 crossrefType "book-chapter" @default.
- W4315567908 hasAuthorship W4315567908A5010928934 @default.
- W4315567908 hasAuthorship W4315567908A5060400468 @default.
- W4315567908 hasAuthorship W4315567908A5065154024 @default.
- W4315567908 hasAuthorship W4315567908A5070703870 @default.
- W4315567908 hasConcept C107568181 @default.
- W4315567908 hasConcept C114073186 @default.
- W4315567908 hasConcept C120314980 @default.
- W4315567908 hasConcept C126255220 @default.
- W4315567908 hasConcept C154945302 @default.
- W4315567908 hasConcept C173608175 @default.
- W4315567908 hasConcept C206729178 @default.
- W4315567908 hasConcept C2776214188 @default.
- W4315567908 hasConcept C31258907 @default.
- W4315567908 hasConcept C33923547 @default.
- W4315567908 hasConcept C41008148 @default.
- W4315567908 hasConcept C48044578 @default.
- W4315567908 hasConcept C5119721 @default.
- W4315567908 hasConcept C55416958 @default.
- W4315567908 hasConcept C68339613 @default.
- W4315567908 hasConcept C74172769 @default.
- W4315567908 hasConcept C77088390 @default.
- W4315567908 hasConcept C83283714 @default.
- W4315567908 hasConcept C97541855 @default.
- W4315567908 hasConceptScore W4315567908C107568181 @default.
- W4315567908 hasConceptScore W4315567908C114073186 @default.
- W4315567908 hasConceptScore W4315567908C120314980 @default.
- W4315567908 hasConceptScore W4315567908C126255220 @default.
- W4315567908 hasConceptScore W4315567908C154945302 @default.
- W4315567908 hasConceptScore W4315567908C173608175 @default.
- W4315567908 hasConceptScore W4315567908C206729178 @default.
- W4315567908 hasConceptScore W4315567908C2776214188 @default.
- W4315567908 hasConceptScore W4315567908C31258907 @default.
- W4315567908 hasConceptScore W4315567908C33923547 @default.
- W4315567908 hasConceptScore W4315567908C41008148 @default.
- W4315567908 hasConceptScore W4315567908C48044578 @default.
- W4315567908 hasConceptScore W4315567908C5119721 @default.
- W4315567908 hasConceptScore W4315567908C55416958 @default.
- W4315567908 hasConceptScore W4315567908C68339613 @default.
- W4315567908 hasConceptScore W4315567908C74172769 @default.
- W4315567908 hasConceptScore W4315567908C77088390 @default.
- W4315567908 hasConceptScore W4315567908C83283714 @default.
- W4315567908 hasConceptScore W4315567908C97541855 @default.
- W4315567908 hasLocation W43155679081 @default.
- W4315567908 hasOpenAccess W4315567908 @default.
- W4315567908 hasPrimaryLocation W43155679081 @default.
- W4315567908 hasRelatedWork W1800827217 @default.
- W4315567908 hasRelatedWork W1992741870 @default.
- W4315567908 hasRelatedWork W2044424083 @default.
- W4315567908 hasRelatedWork W2061535332 @default.
- W4315567908 hasRelatedWork W2136090508 @default.
- W4315567908 hasRelatedWork W2364921833 @default.
- W4315567908 hasRelatedWork W2499279132 @default.
- W4315567908 hasRelatedWork W2546696010 @default.
- W4315567908 hasRelatedWork W3144362044 @default.
- W4315567908 hasRelatedWork W3212584892 @default.
- W4315567908 isParatext "false" @default.
- W4315567908 isRetracted "false" @default.
- W4315567908 workType "book-chapter" @default.