Distributed Ranges: A Model for Building Distributed Data Structures, Algorithms, and Viewsprototypes and early research.Problem: writing parallel programs is hard - Multi-GPU, multi-CPU systems require partitioning data - Users must manually split up data amongst GPUs / nodes - High-level GPU GPU Xe LinkMulti-GPU Systems - NUMA regions: - 4+ GPUs - 2+ CPUs CPU NIC GPU GPU GPU GPU Xe LinkMulti-GPU Systems - NUMA regions: - 4+ GPUs - 2+ CPUs - Systems becoming more hierarchical: GPU Tile 1 Tile 0 Xe LinkProject Goals - Offer high-level, standard C++ distributed data structures - Support distributed algorithms - Achieve high performance for both multi-GPU, NUMA, and multi-node0 码力 | 127 页 | 2.06 MB | 6 月前3
Make Successor Build Systems: World Tour of Build Systemsfile-system caching as the previous level and adds support for distributed cachingCaching Level 4 - Bazel Supports file-system and distributed caching as with the previous level, but based on the digest cache and/or run targets on a shared distributed systemDistributed/remote builds The ability of a build system to cache and/or run targets on a shared distributed system * also known as the thing that that pays my salary :)Distributed builds Level 1 - Make, Ninja, Meson No distributed caching or execution out of the box, everything runs locallyDistributed builds Level 2 - Gradle Has support for0 码力 | 115 页 | 7.02 MB | 6 月前3
POCOAS in C++: A Portable Abstraction for Distributed Data Structurescandidate at Berkeley - Advised by Kathy Yelick and Aydın Buluç - Work on large-scale parallel systems - Use a lot of LBL, ORNL supercomputersThis Talk Background: how do we write a program for a supercomputer supercomputer? Introduce PGAS Model, RDMA Building Remote Pointer Types Building Distributed Data Structures Extending to GPUsThis Talk Background: how do we write a program for a supercomputer? Introduce Introduce PGAS Model, RDMA Building Remote Pointer Types Building Distributed Data Structures Extending to GPUsThis Talk Background: how do we write a program for a supercomputer? Introduce PGAS Model0 码力 | 128 页 | 2.03 MB | 6 月前3
Balancing Efficiency and Flexibility: Cost of Abstractions in Embedded Systems0 码力 | 75 页 | 2.12 MB | 6 月前3
Building Effective Embedded Systems: Architectural Best Practicesbest practices in embedded systems, with a focus on operating systemsToday’s spotlight: Exploring best practices in embedded systems, with a focus on operating systems Today’s take away: Practical Practical tips for building better software, applicable not only to embedded systems but also to software in generalEvery rule presented here comes with an exception Software isn't black and whiteThe code are for illustration⊡ Operating Systems ⊡ Threads ⊡ Layer Separation ⊡ Network Problems ⊡ External Interfaces ⊡ Simulators ⊡ Logs ⊡ Monitoring⊡ Operating Systems ⊡ Threads ⊡ Layer Separation0 码力 | 241 页 | 2.28 MB | 6 月前3
When Nanoseconds Matter: Ultrafast Trading Systems in C++Ultrafast Trading Systems in C++ CppCon 2024 Thursday, 19 September 2024 David GrossIntroduction 2 • About me - Leading the Options Automated Trading Systems team at Optiver market maker company - Worked on low-latency systems for 15 years in Trading and DefenseIntroduction 3 • About me - Leading the Options Automated Trading Systems team at Optiver, a global market maker company company - Worked on low-latency systems for 15 years in Trading and Defense • Today’s talk - Engineering low-latency trading systems - Profiling techniques to find bottlenecks - Some principles along 0 码力 | 123 页 | 5.89 MB | 6 月前3
LLVM's Realtime Safety Revolution: Tools for Modern Mission Critical Systems0 码力 | 153 页 | 1.38 MB | 6 月前3
Spring Boot 2.5.0-M3 Reference Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1. Build Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 4.19. Distributed Transactions with JTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 5.6.2. Supported monitoring systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .0 码力 | 681 页 | 13.41 MB | 1 年前3
Apache ActiveMQ Artemis 2.4.0 User ManualSoftware Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express clustering. Create clusters of servers that know how to load balance messages. Link geographically distributed clusters over unreliable connections to form a global network. Configure routing of messages in0 码力 | 265 页 | 2.84 MB | 1 年前3
Apache ActiveMQ Artemis 2.1.0 User ManualSoftware Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express clustering. Create clusters of servers that know how to load balance messages. Link geographically distributed clusters over unreliable connections to form a global network. Configure routing of messages in0 码力 | 407 页 | 1.93 MB | 1 年前3
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