Modern C++ for Parallelism in High Performance Computing# Poster submission: Modern C++ for Parallelism in High Performance Computing Victor Eijkhout CppCon 2024 ## I ntroduction This poster reports on ‘D2D’, a benchmark that explores elegance of expression ‘mini-application’. The same code has been implemented using a number of different approaches to parallelism. Implementations are discussed with performance results. ## Relevance C++ is making inroads into all outputs are independent, it is perfectly parallelizable. (This is sometimes called 'data parallelism'.) Therefore, achieved parallel speedup will be an important measured quantity in D2D. 2.0 码力 | 3 页 | 91.16 KB | 1 年前3
Interesting Upcoming Features from Low Latency, Parallelism and Concurrency### Interesting Upcoming Features from Low Latency, Parallelism and Concurrency From Kona 2023, Tokyo 2024, and St. Louis 2024 PAUL E. MCKENNEY, MAGED MICHAEL & MICHAEL WONG 20 24 September 15 pointerBringing parallelism to std::ranges algorithms ## I SO C++ Parallelism/Concurrency Programming Language (based on Gonzalo's ISC C++ BoF)  ## Bringing parallelism to std::ranges algorithms (P3179) // C++03 template 0 码力 | 56 页 | 514.85 KB | 1 年前3
Rethinking Task Based Concurrency and Parallelism for Low Latency C++## +24 ## Work Contracts Rethinking Task Based Concurrency and Parallelism for Low Latency C++ MICHAEL A MANISCALCO ### “We cannot solve our problems with the same thinking we used when we created them build on Signal Trees, easy to use, simply design, powerful toolkit for building concurrency and parallelism. ## Thank You ## For listening  Work Contracts Rethinking Task Based Concurrency and Parallelism for Low Latency C++ MICHAEL A MANISCALCO ## Resources: Source Code: github.com/buildingcpp/work_contract0 码力 | 142 页 | 2.80 MB | 1 年前3
Concurrencyto happen before other things Some things can’t happen at the same time synchronization ## PARALLELISM Definition # “Multiple logical threads of execution with no inter-task dependencies” - Daisy EASY Not easy, but a little less hard And it’s really parallelism, not concurrency ### CONCURRENCY MADE EASY Well, less hard. And it’s parallelism, not concurrency ## C++ parallel algorithms Let the C++17 Parallel Algorithms Library and Beyond,” CppCon 2016 David Olsen, “Faster Code Through Parallelism or CPUs and GPUs,” CppCon 2019 # C++ PARALLEL ALGORITHMS Execution policies Add execution policy0 码力 | 160 页 | 2.91 MB | 1 年前3
cppcon 2021 safety guidelines for C parallel and concurrency## +21 ## Misra Parallelism Safety-critical Guidelines for C++11, 17, Then C++20, 23 ANDREAS WEIS, MICHAEL WONG & ILYA BURYLOV 20 21 October 24-29 ## Staff Engineer at Woven Planet 📸 / 🎧 ComicSansMS not safe High Integrity C++ Coding Standard Version 4.0, Programming Research Ltd, 2013 • Some parallelism Software Engineering Institute CERT C/C++ Coding Standard, Software Engineering Institute Division deep analysis of 81 rules • Started in 2019 at a MISRA meeting • Why are there no rules for parallelism in MISRA? • 2019-2021: Phase 1 complete • Reviewed 81 rules pulled from C++CG • HIC++ • REphrase0 码力 | 52 页 | 3.14 MB | 1 年前3
Elasticity and state migration: Part I - CS 591 K1: Data Stream Processing and Analytics Spring 2020\ldots S_{n} $ and rates $ \lambda_{1}, \lambda_{2}, \ldots \lambda_{n} $ identify the minimum parallelism $ \pi_{i} $ per operator i, such that the physical dataflow can sustain all source rates. ## {o}^{k} $$ Optimal parallelism per operator $$ \pi_{i}=\left\lceil\sum_{\forall j:j Optimal parallelism per operator $$ \pi_{i}=\left[\sum_{\forall j:j ## Optimal parallelism per operator $$ \p deployed with optimal parallelism ## Optimal parallelism per operator $$ \pi0 码力 | 93 页 | 2.42 MB | 2 年前3
Designing an ultra low-overhead multithreading runtime for Nimweekend ## Understanding the design space Concurrency vs parallelism, latency vs throughput Cooperative vs preemptive, IO vs CPU ## Parallelism is not concurrence Concurrent = Two Queues One Coffee Machine Hardware vs Software multithreading Data parallelism, Task parallelism, Dataflow parallelism ## Hardware-level multithreading ILP - Instruction-level Parallelism 1 CPU, multiple "execution ports" Data parallelism ## Parallel for loop Same instructions on multiple data OpenMP - Use-cases - Vectors, matrices, multi-dimensional arrays and tensors ## - Challenges: - Nested parallelism - Splitting0 码力 | 37 页 | 556.64 KB | 1 年前3
Back to Basics: Concurrencyanything in computer science under the domain of computer graphics, visualization, concurrency, and parallelism. • Contact information and more on: www.mshah.io ## What you are going to learn today (1/2) (So eb71996fa476915c1f1c2383f3c/p17_2.jpg) ## Parallelism vs Concurrency (programming context) (1/4) Concurrency is often used interchangeably with parallelism--so let’s separate those two terms. 1. Concurrency resources. 2. Parallelism Definition: Everything happens at once, instantaneously ## Parallelism vs Concurrency (programming context) (2/4) Concurrency is often used interchangeably with parallelism--so let’s0 码力 | 141 页 | 6.02 MB | 1 年前3
Fault-tolerance demo & reconfiguration - CS 591 K1: Data Stream Processing and Analytics Spring 2020but fails it immediately. ## Reconfiguration with Savepoints ## Reconfiguration cases • Change parallelism • scale out to process increased load • scale in to save resources • Fix bugs or change business Savepoint • To decrease or increase the parallelism of an application: • Take a savepoint • Cancel the application • Restart it with an adjusted parallelism • The state is automatically redistributed 108e/p29_1.jpg) key ∈ [0, 19] 1. hash(key) = key (identity) 2. subtask(key) = hash(key) % parallelism  ## Can we do0 码力 | 41 页 | 4.09 MB | 2 年前3
Distributed Ranges: A Model for Building Distributed Data Structures, Algorithms, and Viewsf908b376e0dcc67cb1c933cc9ed6c477/p7_1.jpg) ## Outline - Background (Ranges, Parallelism, Distributed Data Structures) - Distributed Ranges (Concepts) - Implementation (Algorithms Data Structures (Dense and sparse matrices) - Lessons learned ## Outline - Background (Ranges, Parallelism, Distributed Data Structures) - Distributed Ranges (Concepts) - Implementation (Algorithms views) - Complex Data Structures (Dense and sparse matrices) - Lessons learned ## Standard C++ Parallelism ## Data structures - Hold and organize data ## - Views - Lightweight objects, views of data0 码力 | 127 页 | 2.06 MB | 1 年前3
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