Assessing and Improving Garbage Collection Performance in the Julia Programming Language
With the increasing popularity of the Julia programming language for memory-intensive applications, garbage collection (GC) is becoming a performance bottleneck, with reports of poor GC performance ranging from differential equation solvers to large database benchmarks. There have been several GC...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/150154 |
| _version_ | 1811087046000771072 |
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| author | Netto, Diogo Correia |
| author2 | Edelman, Alan |
| author_facet | Edelman, Alan Netto, Diogo Correia |
| author_sort | Netto, Diogo Correia |
| collection | MIT |
| description | With the increasing popularity of the Julia programming language for memory-intensive applications, garbage collection (GC) is becoming a performance bottleneck, with reports of poor GC performance ranging from differential equation solvers to large database benchmarks.
There have been several GC optimizations (such as the implementation of a generational collector) targeting the Julia GC over the last decade, but none of them was in the direction of a multithreaded GC.
This thesis assesses GC performance in the Julia programming language and implements optimizations focusing on parallelizing automatic memory management routines. |
| first_indexed | 2024-09-23T13:38:56Z |
| format | Thesis |
| id | mit-1721.1/150154 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:38:56Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1501542023-04-01T03:27:30Z Assessing and Improving Garbage Collection Performance in the Julia Programming Language Netto, Diogo Correia Edelman, Alan Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science With the increasing popularity of the Julia programming language for memory-intensive applications, garbage collection (GC) is becoming a performance bottleneck, with reports of poor GC performance ranging from differential equation solvers to large database benchmarks. There have been several GC optimizations (such as the implementation of a generational collector) targeting the Julia GC over the last decade, but none of them was in the direction of a multithreaded GC. This thesis assesses GC performance in the Julia programming language and implements optimizations focusing on parallelizing automatic memory management routines. M.Eng. 2023-03-31T14:36:11Z 2023-03-31T14:36:11Z 2023-02 2023-02-27T18:43:27.578Z Thesis https://hdl.handle.net/1721.1/150154 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Netto, Diogo Correia Assessing and Improving Garbage Collection Performance in the Julia Programming Language |
| title | Assessing and Improving Garbage Collection Performance in the Julia Programming Language |
| title_full | Assessing and Improving Garbage Collection Performance in the Julia Programming Language |
| title_fullStr | Assessing and Improving Garbage Collection Performance in the Julia Programming Language |
| title_full_unstemmed | Assessing and Improving Garbage Collection Performance in the Julia Programming Language |
| title_short | Assessing and Improving Garbage Collection Performance in the Julia Programming Language |
| title_sort | assessing and improving garbage collection performance in the julia programming language |
| url | https://hdl.handle.net/1721.1/150154 |
| work_keys_str_mv | AT nettodiogocorreia assessingandimprovinggarbagecollectionperformanceinthejuliaprogramminglanguage |