Persistent Hash Map Persistent Transactional Data Structures Live Demonstration ReferencesIntroduction Persistent Hash Map Persistent Transactional Data Structures Live Demonstration References Overview Introduction Data Structures Design Goals Methodology Performance Results Live Demonstration A Persistent Hash Map for Graph Processing Workloads and a Methodology for Persistent Transactional Data Structures 2In 2IntroductionIntroduction Persistent Hash Map Persistent Transactional Data Structures Live Demonstration References Introduction Persistent Memory ▶ Persistent Memory is positioned as a new tier in the

search is now O (log N)! Awesome!!!Problems (Not really, more like caveats …) Like all tree data structures, not really cache friendly. ● Can always represent the tree as a heap but you still jump around

List-like data structures std::vector std::list std::dequestd::vector C++ version of the array-list data structure Backed by a C-style array Automatically allocates a new backing array when inserting

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 Model 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 Model, RDMA Building

0 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 (Ranges, Parallelism, Distributed Data Structures) - Distributed Ranges (Concepts) - Implementation (Algorithms and views) - Complex Data Structures (Dense and sparse matrices) - Lessons (Ranges, Parallelism, Distributed Data Structures) - Distributed Ranges (Concepts) - Implementation (Algorithms and views) - Complex Data Structures (Dense and sparse matrices) - Lessons

Stack/Queue Live Demonstration Design and Implementation of Highly Scalable Quantifiable Data Structures in C++ 2MotivationMotivation Quantifiability Vector Space Entropy Measurement Design and Implementation of atoms in the universe is 1082 Design and Implementation of Highly Scalable Quantifiable Data Structures in C++ 4Motivation Quantifiability Vector Space Entropy Measurement Design and Implementation of takes effect in real-time order. Design and Implementation of Highly Scalable Quantifiable Data Structures in C++ 5Motivation Quantifiability Vector Space Entropy Measurement Design and Implementation of

19 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 10 Control Flow 94 10.1 Compound Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logical operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 21.4 Control Flow and Short-Circuiting Operators . . . . . . . . . . . . . . . . . . . . . . 296 21.5 Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . 462 35.15 Use MutableArithmetics for more control over allocation for mutable arithmetic types463 35.16 More dots: Fuse vectorized operations . .

19 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 10 Control Flow 94 10.1 Compound Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logical operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 21.4 Control Flow and Short-Circuiting Operators . . . . . . . . . . . . . . . . . . . . . . 296 21.5 Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . 462 35.15 Use MutableArithmetics for more control over allocation for mutable arithmetic types463 35.16 More dots: Fuse vectorized operations . .

19 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 10 Control Flow 94 10.1 Compound Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logical operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 21.4 Control Flow and Short-Circuiting Operators . . . . . . . . . . . . . . . . . . . . . . 296 21.5 Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . 462 35.15 Use MutableArithmetics for more control over allocation for mutable arithmetic types463 35.16 More dots: Fuse vectorized operations . .