Foreword

• Humongous computing needs: Science years in the making
• Open standards
• Keen on many-core architecture
• Xeon Phi is born: Many cores, excellent vector ISA
• Learn highly scalable parallel programming
• Future demands grow: Programming models matter

Preface

• Inspired by 61 cores: A new era in programming

Chapter 1: Introduction

• Abstract
• Learning from successful experiences
• Code modernization
• Modernize with concurrent algorithms
• Modernize with vectorization and data locality
• Understanding power usage
• ISPC and OpenCL anyone?
• Intel Xeon Phi coprocessor specific
• Many-core, neo-heterogeneous
• No “Xeon Phi¿ in the title, neo-heterogeneous programming
• The future of many-core
• Downloads

Chapter 2: From “Correct¿ to “Correct & Efficient¿: A Hydro2D Case Study with Godunov’s Scheme

• Abstract
• Scientific computing on contemporary computers
• A numerical method for shock hydrodynamics
• Features of modern architectures
• Paths to performance
• Summary

Chapter 3: Better Concurrency and SIMD on HBM

• Abstract
• The application: HIROMB-BOOS-Model
• Key usage: DMI
• HBM execution profile
• Overview for the optimization of HBM
• Data structures: Locality done right
• Thread parallelism in HBM
• Data parallelism: SIMD vectorization
• Results
• Profiling details
• Scaling on processor vs. coprocessor
• Contiguous attribute
• Summary

Chapter 4: Optimizing for Reacting Navier-Stokes Equations

• Abstract
• Getting started
• Version 1.0: Baseline
• Version 2.0: ThreadBox
• Version 3.0: Stack memory
• Version 4.0: Blocking
• Version 5.0: Vectorization
• Intel Xeon Phi coprocessor results
• Summary

Chapter 5: Plesiochronous Phasing Barriers

• Abstract
• What can be done to improve the code?
• What more can be done to improve the code?
• Hyper-Thread Phalanx
• What is nonoptimal about this strategy?
• Coding the Hyper-Thread Phalanx
• Back to work
• Data alignment
• The plesiochronous phasing barrier
• Let us do something to recover this wasted time
• A few “left to the reader¿ possibilities
• Xeon host performance improvements similar to Xeon Phi
• Summary

Chapter 6: Parallel Evaluation of Fault Tree Expressions

• Abstract
• Motivation and background
• Example implementation
• Other considerations
• Summary

Chapter 7: Deep-Learning Numerical Optimization

• Abstract
• Fitting an objective function
• Objective functions and principle components analysis
• Software and example data
• Training data
• Runtime results
• Scaling results
• Summary

Chapter 8: Optimizing Gather/Scatter Patterns

• Abstract
• Gather/scatter instructions in Intel® architecture
• Gather/scatter patterns in molecular dynamics
• Optimizing gather/scatter patterns
• Summary

Chapter 9: A Many-Core Implementation of the Direct N-Body Problem

• Abstract
• N-Body simulations
• Initial solution
• Theoretical limit
• Reduce the overheads, align your data
• Optimize the memory hierarchy
• Improving our tiling
• What does all this mean to the host version?
• Summary

Chapter 10: N-Body Methods

• Abstract
• Fast N-body methods and direct N-body kernels
• Applications of N-body methods
• Direct N-body code
• Performance results
• Summary

Chapter 11: Dynamic Load Balancing Using OpenMP 4.0

• Abstract
• Maximizing hardware usage
• The N-Body kernel
• The offloaded version
• A first processor combined with coprocessor version
• Version for processor with multiple coprocessors

Chapter 12: Concurrent Kernel Offloading

• Abstract
• Setting the context
• Concurrent kernels on the coprocessor
• Force computation in PD using concurrent kernel offloading
• The bottom line

Chapter 13: Heterogeneous Computing with MPI

• Abstract
• Acknowledgments
• MPI in the modern clusters
• MPI task location
• Selection of the DAPL providers
• Summary

Chapter 14: Power Analysis on the Intel® Xeon Phi™ Coprocessor

• Abstract
• Power analysis 101
• Measuring power and temperature with software
• Hardware-based power analysis methods
• Summary

Chapter 15: Integrating Intel Xeon Phi Coprocessors into a Cluster Environment

• Abstract
• Acknowledgments
• Early explorations
• Beacon system history
• Beacon system architecture
• Intel MPSS installation procedure
• Setting up the resource and workload managers
• Health checking and monitoring
• Scripting common commands
• User software environment
• Future directions
• Summary

Chapter 16: Supporting Cluster File Systems on Intel® Xeon Phi™ Coprocessors

• Abstract
• Network configuration concepts and goals
• Coprocessor file systems support
• Summary

Chapter 17: NWChem: Quantum Chemistry Simulations at Scale

• Abstract
• Introduction
• Overview of single-reference CC formalism
• NWChem software architecture
• Engineering an offload solution
• Offload architecture
• Kernel optimizations
• Performance evaluation
• Summary
• Acknowledgments

Chapter 18: Efficient Nested Parallelism on Large-Scale Systems

• Abstract
• Motivation
• The benchmark
• Baseline benchmarking
• Pipeline approach—flat_arena class
• Intel® TBB user-managed task arenas
• Hierarchical approach—hierarchical_arena class
• Performance evaluation
• Implication on NUMA architectures
• Summary

Chapter 19: Performance Optimization of Black-Scholes Pricing

• Abstract
• Financial market model basics and the Black-Scholes formula
• Case study
• Summary

Chapter 20: Data Transfer Using the Intel COI Library

• Abstract
• First steps with the Intel COI library
• COI buffer types and transfer performance
• Applications
• Summary

Chapter 21: High-Performance Ray Tracing

• Abstract
• Background
• Vectorizing ray traversal
• The Embree ray tracing kernels
• Using Embree in an application
• Performance
• Summary

Chapter 22: Portable Performance with OpenCL

• Abstract
• The dilemma
• A brief introduction to OpenCL
• A matrix multiply example in OpenCL
• OpenCL and the Intel Xeon Phi Coprocessor
• Matrix multiply performance results
• Case study: Molecular docking
• Results: Portable performance
• Related work
• Summary

Chapter 23: Characterization and Optimization Methodology Applied to Stencil Computations

• Abstract
• Introduction
• Performance evaluation
• Standard optimizations
• Summary

Chapter 24: Profiling-Guided Optimization

• Abstract
• Matrix transposition in computer science
• Tools and methods
• “Serial¿: Our original in-place transposition
• “Parallel¿: Adding parallelism with OpenMP
• “Tiled¿: Improving data locality
• “Regularized¿: Microkernel with multiversioning
• “Planned¿: Exposing more parallelism
• Summary

Chapter 25: Heterogeneous MPI application optimization with ITAC

• Abstract
• Asian options pricing
• Application design
• Synchronization in heterogeneous clusters
• Finding bottlenecks with ITAC
• Setting up ITAC
• Unbalanced MPI run
• Manual workload balance
• Dynamic “Boss-Workers¿ load balancing
• Conclusion

Chapter 26: Scalable Out-of-Core Solvers on a Cluster

• Abstract
• Introduction
• An OOC factorization based on ScaLAPACK
• Porting from NVIDIA GPU to the Intel Xeon Phi coprocessor
• Numerical results
• Conclusions and future work
• Acknowledgments

Chapter 27: Sparse Matrix-Vector Multiplication: Parallelization and Vectorization

• Abstract
• Acknowledgments
• Background
• Sparse matrix data structures
• Parallel SpMV multiplication
• Vectorization on the Intel Xeon Phi coprocessor
• Evaluation
• Summary

Chapter 28: Morton Order Improves Performance

• Abstract
• Improving cache locality by data ordering
• Improving performance
• Matrix transpose
• Matrix multiply
• Summary