#parallelism — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #parallelism, aggregated by home.social.
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UpDown: Efficient Manycore based on Many Threading & Scalable Memory Parallelism
https://people.cs.uchicago.edu/~aachien/lssg/research/10x10/ics26-single-chip-updown.pdf
#HackerNews #Manycore #ManyThreading #ScalableMemory #Parallelism #Research #Paper #HardwareOptimization
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UpDown: Efficient Manycore based on Many Threading & Scalable Memory Parallelism
https://people.cs.uchicago.edu/~aachien/lssg/research/10x10/ics26-single-chip-updown.pdf
#HackerNews #Manycore #ManyThreading #ScalableMemory #Parallelism #Research #Paper #HardwareOptimization
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UpDown: Efficient Manycore based on Many Threading & Scalable Memory Parallelism
https://people.cs.uchicago.edu/~aachien/lssg/research/10x10/ics26-single-chip-updown.pdf
#HackerNews #Manycore #ManyThreading #ScalableMemory #Parallelism #Research #Paper #HardwareOptimization
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UpDown: Efficient Manycore based on Many Threading & Scalable Memory Parallelism
https://people.cs.uchicago.edu/~aachien/lssg/research/10x10/ics26-single-chip-updown.pdf
#HackerNews #Manycore #ManyThreading #ScalableMemory #Parallelism #Research #Paper #HardwareOptimization
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UpDown: Efficient Manycore based on Many Threading & Scalable Memory Parallelism
https://people.cs.uchicago.edu/~aachien/lssg/research/10x10/ics26-single-chip-updown.pdf
#HackerNews #Manycore #ManyThreading #ScalableMemory #Parallelism #Research #Paper #HardwareOptimization
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🤔Are #convergence and #parallelism two different phenomena?
🤗Using examples from plant domestication and specialized #metabolism, Scossa et al. talk definitions and explain how complex traits #evolve repeatedly in #plants.
👉https://doi.org/10.1111/jipb.70236
@WileyLifeSci
#PlantSci #JIPB #botany -
🤔Are #convergence and #parallelism two different phenomena?
🤗Using examples from plant domestication and specialized #metabolism, Scossa et al. talk definitions and explain how complex traits #evolve repeatedly in #plants.
👉https://doi.org/10.1111/jipb.70236
@WileyLifeSci
#PlantSci #JIPB #botany -
Nebenläufige Programmierung ist ein Begriff, der in der Softwareentwicklung häufig fällt, aber oft unterschiedlich interpretiert wird. Grundsätzlich beschreibt er die Fähigkeit eines Programms, mehrere Aufgaben gleichzeitig oder scheinbar gleichzeitig a...
https://magicmarcy.de/nebenlaeufige-programmierung
#Nebenläufige_Programmierung #Softwareentwicklung #Aufgaben #gleichzeitig #Nebenläufigkeit #Concurrency #Parallelität #Parallelism #sequentielle_Verarbeitung #Ausführungsreihenfolge
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Nebenläufige Programmierung ist ein Begriff, der in der Softwareentwicklung häufig fällt, aber oft unterschiedlich interpretiert wird. Grundsätzlich beschreibt er die Fähigkeit eines Programms, mehrere Aufgaben gleichzeitig oder scheinbar gleichzeitig a...
https://magicmarcy.de/nebenlaeufige-programmierung
#Nebenläufige_Programmierung #Softwareentwicklung #Aufgaben #gleichzeitig #Nebenläufigkeit #Concurrency #Parallelität #Parallelism #sequentielle_Verarbeitung #Ausführungsreihenfolge
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Nebenläufige Programmierung ist ein Begriff, der in der Softwareentwicklung häufig fällt, aber oft unterschiedlich interpretiert wird. Grundsätzlich beschreibt er die Fähigkeit eines Programms, mehrere Aufgaben gleichzeitig oder scheinbar gleichzeitig a...
https://magicmarcy.de/nebenlaeufige-programmierung
#Nebenläufige_Programmierung #Softwareentwicklung #Aufgaben #gleichzeitig #Nebenläufigkeit #Concurrency #Parallelität #Parallelism #sequentielle_Verarbeitung #Ausführungsreihenfolge
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Nebenläufige Programmierung ist ein Begriff, der in der Softwareentwicklung häufig fällt, aber oft unterschiedlich interpretiert wird. Grundsätzlich beschreibt er die Fähigkeit eines Programms, mehrere Aufgaben gleichzeitig oder scheinbar gleichzeitig a...
https://magicmarcy.de/nebenlaeufige-programmierung
#Nebenläufige_Programmierung #Softwareentwicklung #Aufgaben #gleichzeitig #Nebenläufigkeit #Concurrency #Parallelität #Parallelism #sequentielle_Verarbeitung #Ausführungsreihenfolge
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[Перевод] Разница между параллельными и распределёнными вычислениями
Параллельные и распределённые вычисления часто ставят рядом, но это далеко не одно и то же. В новом переводе от команды Spring АйО разберем, как устроены обе модели, чем отличаются их архитектура, способы обмена данными, масштабируемость и отказоустойчивость. Статья подойдет тем, кто хочет понять, когда достаточно ресурсов одной машины, а когда без сети из нескольких узлов уже не обойтись.
https://habr.com/ru/companies/spring_aio/articles/1008990/
#system_design #consistency #distributed_computing #distributed_systems #distributed #parallels #parallelism #parallel_computing #spring #spring_boot
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[Перевод] Разница между параллельными и распределёнными вычислениями
Параллельные и распределённые вычисления часто ставят рядом, но это далеко не одно и то же. В новом переводе от команды Spring АйО разберем, как устроены обе модели, чем отличаются их архитектура, способы обмена данными, масштабируемость и отказоустойчивость. Статья подойдет тем, кто хочет понять, когда достаточно ресурсов одной машины, а когда без сети из нескольких узлов уже не обойтись.
https://habr.com/ru/companies/spring_aio/articles/1008990/
#system_design #consistency #distributed_computing #distributed_systems #distributed #parallels #parallelism #parallel_computing #spring #spring_boot
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[Перевод] Разница между параллельными и распределёнными вычислениями
Параллельные и распределённые вычисления часто ставят рядом, но это далеко не одно и то же. В новом переводе от команды Spring АйО разберем, как устроены обе модели, чем отличаются их архитектура, способы обмена данными, масштабируемость и отказоустойчивость. Статья подойдет тем, кто хочет понять, когда достаточно ресурсов одной машины, а когда без сети из нескольких узлов уже не обойтись.
https://habr.com/ru/companies/spring_aio/articles/1008990/
#system_design #consistency #distributed_computing #distributed_systems #distributed #parallels #parallelism #parallel_computing #spring #spring_boot
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[Перевод] Разница между параллельными и распределёнными вычислениями
Параллельные и распределённые вычисления часто ставят рядом, но это далеко не одно и то же. В новом переводе от команды Spring АйО разберем, как устроены обе модели, чем отличаются их архитектура, способы обмена данными, масштабируемость и отказоустойчивость. Статья подойдет тем, кто хочет понять, когда достаточно ресурсов одной машины, а когда без сети из нескольких узлов уже не обойтись.
https://habr.com/ru/companies/spring_aio/articles/1008990/
#system_design #consistency #distributed_computing #distributed_systems #distributed #parallels #parallelism #parallel_computing #spring #spring_boot
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Khóa học về đồng thời và song song: Concurrency và Parallelism là gì? Concurrency là xử lý nhiều việc một lúc, trong khi Parallelism là làm nhiều việc cùng một lúc. Hiểu được sự khác biệt này giúp cải thiện khả năng mở rộng của SAAS #Concurrency #Parallelism #SAAS #Scalability #LậpTrình #PhátTriểnngDụng #TăngNăngSuất #ĐồngThời #SongSong
https://www.reddit.com/r/SaaS/comments/1p4koom/the_same_same_but_different_trap_concurrency_vs/
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Пул интерпретаторов в Python 3.14. Что, зачем и почему?
Как все знают, GIL (Global Interpreter Lock) не позволяет нескольким потокам CPython выполнять CPU-bound задачи параллельно. Глобальная блокировка интерпретатора предоставляет каждому потоку лишь небольшой интервал времени для работы. При этом планирование работы потоков (какому именно потоку из ожидающих предоставить разрешение на выполнение) осуществляется планировщиком операционной системы. Интерпретатор не является полноценным планировщиком работы потоков, он делегирует эту функцию операционной системе. GIL использует мьютексы ОС для блокировки работы потоков так, чтобы в один момент времени мог выполняться только один поток из нескольких.
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Пул интерпретаторов в Python 3.14. Что, зачем и почему?
Как все знают, GIL (Global Interpreter Lock) не позволяет нескольким потокам CPython выполнять CPU-bound задачи параллельно. Глобальная блокировка интерпретатора предоставляет каждому потоку лишь небольшой интервал времени для работы. При этом планирование работы потоков (какому именно потоку из ожидающих предоставить разрешение на выполнение) осуществляется планировщиком операционной системы. Интерпретатор не является полноценным планировщиком работы потоков, он делегирует эту функцию операционной системе. GIL использует мьютексы ОС для блокировки работы потоков так, чтобы в один момент времени мог выполняться только один поток из нескольких.
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Пул интерпретаторов в Python 3.14. Что, зачем и почему?
Как все знают, GIL (Global Interpreter Lock) не позволяет нескольким потокам CPython выполнять CPU-bound задачи параллельно. Глобальная блокировка интерпретатора предоставляет каждому потоку лишь небольшой интервал времени для работы. При этом планирование работы потоков (какому именно потоку из ожидающих предоставить разрешение на выполнение) осуществляется планировщиком операционной системы. Интерпретатор не является полноценным планировщиком работы потоков, он делегирует эту функцию операционной системе. GIL использует мьютексы ОС для блокировки работы потоков так, чтобы в один момент времени мог выполняться только один поток из нескольких.
-
Пул интерпретаторов в Python 3.14. Что, зачем и почему?
Как все знают, GIL (Global Interpreter Lock) не позволяет нескольким потокам CPython выполнять CPU-bound задачи параллельно. Глобальная блокировка интерпретатора предоставляет каждому потоку лишь небольшой интервал времени для работы. При этом планирование работы потоков (какому именно потоку из ожидающих предоставить разрешение на выполнение) осуществляется планировщиком операционной системы. Интерпретатор не является полноценным планировщиком работы потоков, он делегирует эту функцию операционной системе. GIL использует мьютексы ОС для блокировки работы потоков так, чтобы в один момент времени мог выполняться только один поток из нескольких.
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Introducing the most riveting tale of all time: the #love #story between a #GPU and its #graphics, sprinkled with just enough #Triton #jargon to make you nod off faster than a PyTorch Profiler. We've got #parallelism, pheromones, and more tangents than a high school geometry class 💤. Pack your bags, folks, because we're going on an #adventure through a sea of terrifying colors and kernels that nobody asked for! 🚀🌈
https://ut21.github.io/blog/triton.html #HackerNews #ngated -
Introducing the most riveting tale of all time: the #love #story between a #GPU and its #graphics, sprinkled with just enough #Triton #jargon to make you nod off faster than a PyTorch Profiler. We've got #parallelism, pheromones, and more tangents than a high school geometry class 💤. Pack your bags, folks, because we're going on an #adventure through a sea of terrifying colors and kernels that nobody asked for! 🚀🌈
https://ut21.github.io/blog/triton.html #HackerNews #ngated -
Introducing the most riveting tale of all time: the #love #story between a #GPU and its #graphics, sprinkled with just enough #Triton #jargon to make you nod off faster than a PyTorch Profiler. We've got #parallelism, pheromones, and more tangents than a high school geometry class 💤. Pack your bags, folks, because we're going on an #adventure through a sea of terrifying colors and kernels that nobody asked for! 🚀🌈
https://ut21.github.io/blog/triton.html #HackerNews #ngated -
Introducing the most riveting tale of all time: the #love #story between a #GPU and its #graphics, sprinkled with just enough #Triton #jargon to make you nod off faster than a PyTorch Profiler. We've got #parallelism, pheromones, and more tangents than a high school geometry class 💤. Pack your bags, folks, because we're going on an #adventure through a sea of terrifying colors and kernels that nobody asked for! 🚀🌈
https://ut21.github.io/blog/triton.html #HackerNews #ngated -
Arendt et al. discuss several metrics of phenotypic parallel evolution including one that does not actually measure parallelism. They suggest use of a partial Eta-squared of the interaction term between Habitat and Replicate as an appropriate metric.
Read now ahead of print!
https://www.journals.uchicago.edu/doi/10.1086/736845#evolution #parallelism #habitat #phenotypes #parallelEvolution
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Arendt et al. discuss several metrics of phenotypic parallel evolution including one that does not actually measure parallelism. They suggest use of a partial Eta-squared of the interaction term between Habitat and Replicate as an appropriate metric.
Read now ahead of print!
https://www.journals.uchicago.edu/doi/10.1086/736845#evolution #parallelism #habitat #phenotypes #parallelEvolution
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Arendt et al. discuss several metrics of phenotypic parallel evolution including one that does not actually measure parallelism. They suggest use of a partial Eta-squared of the interaction term between Habitat and Replicate as an appropriate metric.
Read now ahead of print!
https://www.journals.uchicago.edu/doi/10.1086/736845#evolution #parallelism #habitat #phenotypes #parallelEvolution
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Arendt et al. discuss several metrics of phenotypic parallel evolution including one that does not actually measure parallelism. They suggest use of a partial Eta-squared of the interaction term between Habitat and Replicate as an appropriate metric.
Read now ahead of print!
https://www.journals.uchicago.edu/doi/10.1086/736845#evolution #parallelism #habitat #phenotypes #parallelEvolution
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Arendt et al. discuss several metrics of phenotypic parallel evolution including one that does not actually measure parallelism. They suggest use of a partial Eta-squared of the interaction term between Habitat and Replicate as an appropriate metric.
Read now ahead of print!
https://www.journals.uchicago.edu/doi/10.1086/736845#evolution #parallelism #habitat #phenotypes #parallelEvolution
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Concurrency Concept: Say you have two threads trying to access the same bytes.
Instead of a lock, both threads can reserve a specific time interval, in which they are allowed to access the memory area. So the access timeline would look something like this (t1 and t2 being the timeframe's the respective threads can access it): t1 | t2 | t1 | t2 | ... -
Concurrency Concept: Say you have two threads trying to access the same bytes.
Instead of a lock, both threads can reserve a specific time interval, in which they are allowed to access the memory area. So the access timeline would look something like this (t1 and t2 being the timeframe's the respective threads can access it): t1 | t2 | t1 | t2 | ... -
Concurrency Concept: Say you have two threads trying to access the same bytes.
Instead of a lock, both threads can reserve a specific time interval, in which they are allowed to access the memory area. So the access timeline would look something like this (t1 and t2 being the timeframe's the respective threads can access it): t1 | t2 | t1 | t2 | ... -
Concurrency Concept: Say you have two threads trying to access the same bytes.
Instead of a lock, both threads can reserve a specific time interval, in which they are allowed to access the memory area. So the access timeline would look something like this (t1 and t2 being the timeframe's the respective threads can access it): t1 | t2 | t1 | t2 | ... -
Concurrency Concept: Say you have two threads trying to access the same bytes.
Instead of a lock, both threads can reserve a specific time interval, in which they are allowed to access the memory area. So the access timeline would look something like this (t1 and t2 being the timeframe's the respective threads can access it): t1 | t2 | t1 | t2 | ... -
Concurrency and parallelism are often confused in async programming discussions. Go's goroutines highlighted the difference: concurrency is doing many things at once, while parallelism is doing many things at the same time.
AsyncIO handles concurrency well for I/O, but CPU-bound tasks need parallelism. Python uses AsyncIO for concurrency, and ProcessPoolExecutor for parallelism, distributing work across CPU cores.
Process communication is harder than thread communication. AsyncIO's task cancellation differs from ProcessPoolExecutor's, requiring workarounds like event objects for reliable cancellation and shutdown.
Essentially, ProcessPoolExecutor enables parallelism for CPU-bound tasks, scaling them across multiple cores, while AsyncIO handles I/O concurrently.
#python #asyncio #concurrency #parallelism #multiprocessing #opentowork #getfedihired #fedihire #opentowork
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Concurrency and parallelism are often confused in async programming discussions. Go's goroutines highlighted the difference: concurrency is doing many things at once, while parallelism is doing many things at the same time.
AsyncIO handles concurrency well for I/O, but CPU-bound tasks need parallelism. Python uses AsyncIO for concurrency, and ProcessPoolExecutor for parallelism, distributing work across CPU cores.
Process communication is harder than thread communication. AsyncIO's task cancellation differs from ProcessPoolExecutor's, requiring workarounds like event objects for reliable cancellation and shutdown.
Essentially, ProcessPoolExecutor enables parallelism for CPU-bound tasks, scaling them across multiple cores, while AsyncIO handles I/O concurrently.
#python #asyncio #concurrency #parallelism #multiprocessing #opentowork #getfedihired #fedihire #opentowork
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Concurrency and parallelism are often confused in async programming discussions. Go's goroutines highlighted the difference: concurrency is doing many things at once, while parallelism is doing many things at the same time.
AsyncIO handles concurrency well for I/O, but CPU-bound tasks need parallelism. Python uses AsyncIO for concurrency, and ProcessPoolExecutor for parallelism, distributing work across CPU cores.
Process communication is harder than thread communication. AsyncIO's task cancellation differs from ProcessPoolExecutor's, requiring workarounds like event objects for reliable cancellation and shutdown.
Essentially, ProcessPoolExecutor enables parallelism for CPU-bound tasks, scaling them across multiple cores, while AsyncIO handles I/O concurrently.
#python #asyncio #concurrency #parallelism #multiprocessing #opentowork #getfedihired #fedihire #opentowork
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Concurrency and parallelism are often confused in async programming discussions. Go's goroutines highlighted the difference: concurrency is doing many things at once, while parallelism is doing many things at the same time.
AsyncIO handles concurrency well for I/O, but CPU-bound tasks need parallelism. Python uses AsyncIO for concurrency, and ProcessPoolExecutor for parallelism, distributing work across CPU cores.
Process communication is harder than thread communication. AsyncIO's task cancellation differs from ProcessPoolExecutor's, requiring workarounds like event objects for reliable cancellation and shutdown.
Essentially, ProcessPoolExecutor enables parallelism for CPU-bound tasks, scaling them across multiple cores, while AsyncIO handles I/O concurrently.
#python #asyncio #concurrency #parallelism #multiprocessing #opentowork #getfedihired #fedihire #opentowork
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🏃♀️ High-performance computing, with much less code
(... back in my business development days I was interested in "rules and schemes" that would separate what is to be done with how it should be done)
https://news.mit.edu/2025/high-performance-computing-with-much-less-code-0313
#computing #software #programming #languages #hpc #parallelism
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🏃♀️ High-performance computing, with much less code
(... back in my business development days I was interested in "rules and schemes" that would separate what is to be done with how it should be done)
https://news.mit.edu/2025/high-performance-computing-with-much-less-code-0313
#computing #software #programming #languages #hpc #parallelism
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🏃♀️ High-performance computing, with much less code
(... back in my business development days I was interested in "rules and schemes" that would separate what is to be done with how it should be done)
https://news.mit.edu/2025/high-performance-computing-with-much-less-code-0313
#computing #software #programming #languages #hpc #parallelism
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🏃♀️ High-performance computing, with much less code
(... back in my business development days I was interested in "rules and schemes" that would separate what is to be done with how it should be done)
https://news.mit.edu/2025/high-performance-computing-with-much-less-code-0313
#computing #software #programming #languages #hpc #parallelism
-
🏃♀️ High-performance computing, with much less code
(... back in my business development days I was interested in "rules and schemes" that would separate what is to be done with how it should be done)
https://news.mit.edu/2025/high-performance-computing-with-much-less-code-0313
#computing #software #programming #languages #hpc #parallelism
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Speeding Up Computational Lithography with the Power and Parallelism of GPUs — https://semiengineering.com/speeding-up-computational-lithography-with-the-power-and-parallelism-of-gpus/
#HackerNews #ComputationalLithography #GPUs #Parallelism #TechInnovation #Semiconductor -
Speeding Up Computational Lithography with the Power and Parallelism of GPUs — https://semiengineering.com/speeding-up-computational-lithography-with-the-power-and-parallelism-of-gpus/
#HackerNews #ComputationalLithography #GPUs #Parallelism #TechInnovation #Semiconductor -
Speeding Up Computational Lithography with the Power and Parallelism of GPUs — https://semiengineering.com/speeding-up-computational-lithography-with-the-power-and-parallelism-of-gpus/
#HackerNews #ComputationalLithography #GPUs #Parallelism #TechInnovation #Semiconductor -
Speeding Up Computational Lithography with the Power and Parallelism of GPUs — https://semiengineering.com/speeding-up-computational-lithography-with-the-power-and-parallelism-of-gpus/
#HackerNews #ComputationalLithography #GPUs #Parallelism #TechInnovation #Semiconductor -
DeepSeek Open Source Optimized Parallelism Strategies, 3 repos — https://github.com/deepseek-ai/profile-data
#HackerNews #DeepSeek #Open #Source #Optimized #Parallelism #Strategies #3 #repos #"DeepSeek" #"OpenSource" #"Parallelism" #"AI" #"GitHub" -
DeepSeek Open Source Optimized Parallelism Strategies, 3 repos — https://github.com/deepseek-ai/profile-data
#HackerNews #DeepSeek #Open #Source #Optimized #Parallelism #Strategies #3 #repos #"DeepSeek" #"OpenSource" #"Parallelism" #"AI" #"GitHub" -
DeepSeek Open Source Optimized Parallelism Strategies, 3 repos — https://github.com/deepseek-ai/profile-data
#HackerNews #DeepSeek #Open #Source #Optimized #Parallelism #Strategies #3 #repos #"DeepSeek" #"OpenSource" #"Parallelism" #"AI" #"GitHub"