Intel® Advisor User Guide

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Document Table of Contents
Document Table of Contents x
Intel® Advisor User Guide
Intel® Advisor User Guide x
Introduction Install and Launch Intel® Advisor Set Up Project Analyze Vectorization Perspective Analyze CPU Roofline Model Threading Designs Model Offloading to a GPU Analyze GPU Roofline Design and Analyze Flow Graphs Minimize Analysis Overhead Analyze MPI Applications Manage Results Command Line Interface Troubleshooting Reference Appendix
Introduction x
Design and Optimization Methodology Tutorials and Samples Get Help and Support
Install and Launch Intel® Advisor x
Install Intel® Advisor Set Up Environment Variables Set Up System to Analyze GPU Kernels Set Up Environment to Offload SYCL, OpenMP* target, and OpenCL™ Applications to CPU Launch Intel® Advisor GUI Navigation Quick Start
Set Up Project x
Configure Target Application Build Target Application Create Project
Configure Target Application x
Limit the Number of Threads Used by Parallel Frameworks Choose a Small, Representative Data Set
Create Project x
Configure Project Configure Binary/Symbol Search Directories Configure Source Search Directory Binary/Symbol Search and Source Search Locations
Analyze Vectorization Perspective x
Run Vectorization and Code Insights Perspective from GUI Run Vectorization and Code Insights Perspective from Command Line Explore Vectorization and Code Insights Results
Run Vectorization and Code Insights Perspective from GUI x
Vectorization Accuracy Presets Customize Vectorization and Code Insights Perspective
Run Vectorization and Code Insights Perspective from Command Line x
Vectorization Accuracy Levels in Command Line
Explore Vectorization and Code Insights Results x
Vectorization Report Overview Examine Not-Vectorized and Under-Vectorized Loops Analyze Loop Call Count Investigate Memory Usage and Traffic Find Data Dependencies
Analyze CPU Roofline x
Run CPU / Memory Roofline Insights Perspective from GUI Run CPU / Memory Roofline Insights Perspective from Command Line Explore CPU/Memory Roofline Results
Run CPU / Memory Roofline Insights Perspective from GUI x
CPU Roofline Accuracy Presets Customize CPU / Memory Roofline Insights Perspective
Run CPU / Memory Roofline Insights Perspective from Command Line x
CPU Roofline Accuracy Levels in Command Line
Explore CPU/Memory Roofline Results x
CPU Roofline Report Overview Examine Bottlenecks on CPU Roofline Chart Examine Relationships Between Memory Levels Compare CPU Roofline Results
Model Threading Designs x
Run Threading Perspective from GUI Run Threading Perspective from Command Line Annotate Code for Deeper Analysis Explore Threading Results Model Threading Parallelism Check for Dependencies Issues Add Parallelism to Your Program
Run Threading Perspective from GUI x
Customize Threading Perspective
Run Threading Perspective from Command Line x
Threading Accuracy Levels in Command Line
Annotate Code for Deeper Analysis x
Annotate Code to Model Parallelism Annotations Annotation Report
Annotate Code to Model Parallelism x
Before Annotating Code for Deeper Analysis Use Amdahl's Law and Measure the Program Task Organization and Annotations Annotate Parallel Sites and Tasks Task Patterns Choose the Tasks Use Partially Parallel Programs with Intel® Advisor
Task Patterns x
Multiple Parallel Sites Data and Task Parallelism Mix and Match Tasks
Choose the Tasks x
Task Interactions and Suitability How Big Should a Task Be?
Annotations x
Annotation Types Annotation Definitions Files Add Annotations into Your Source Code Tips for Annotation Use with C/C++ Programs
Annotation Types x
Annotation Types Summary Annotation General Characteristics Site and Task Annotations for Simple Loops With One Task Site and Task Annotations for Parallel Sites with Multiple Tasks Lock Annotations Pause Collection and Resume Collection Annotations Special-purpose Annotations
Annotation Definitions Files x
Reference the Annotations Definitions Directory Include the Annotations Header File in C/C++ Sources
Add Annotations into Your Source Code x
Copy Annotations and Build Settings Using the Annotation Assistant Pane Insert Annotations in a Text Editor
Tips for Annotation Use with C/C++ Programs x
Control the Expansion of advisor-annotate.h Handle Compilation Issues that Appear After Adding advisor-annotate.h
Annotation Report x
Annotation Report, Clear Description of Storage Row Annotation Report, Disable Observations in Region Row Annotation Report, Pause Collection Row Annotation Report, Inductive Expression Row Annotation Report, Lock Row Annotation Report, Observe Uses Row Annotation Report, Reduction Row Annotation Report, Re-enable Observations at End of Region Row Annotation Report, Resume Collection Row Annotation Report, Site Row Annotation Report, Task Row Annotation Report, User Memory Allocator Use Row Annotation Report, User Memory Deallocator Use Row
Model Threading Parallelism x
Suitability Report Overview Choose Modeling Parameters in the Suitability Report Fix Annotation-related Errors Detected by the Suitability Tool Advanced Modeling Options Reduce Parallel Overhead, Lock Contention, and Enable Chunking
Reduce Parallel Overhead, Lock Contention, and Enable Chunking x
Reduce Site Overhead Reduce Task Overhead Reduce Lock Overhead Reduce Lock Contention Enable Task Chunking
Check for Dependencies Issues x
Code Locations Pane Filter Pane (Dependencies Report) Problems and Messages Pane Dependencies Source Window
Dependencies Source Window x
Code Locations Pane (Dependencies Source Window) Focus Code Location Pane Focus Code Location Call Stack Pane Related Code Locations Pane Related Code Location Call Stack Pane Relationship Diagram Pane
Add Parallelism to Your Program x
Before You Add Parallelism: Choose a Parallel Framework Add the Parallel Framework to Your Build Environment Annotation Report Replace Annotations with Intel® oneAPI Threading Building Blocks (oneTBB) Code Replace Annotations with OpenMP* Code Next Steps for the Parallel Program
Before You Add Parallelism: Choose a Parallel Framework x
Parallel Frameworks Intel® oneAPI Threading Building Blocks (oneTBB) OpenMP* Microsoft Task Parallel Library* (TPL) Other Parallel Frameworks
Add the Parallel Framework to Your Build Environment x
Enable Intel® oneAPI Threading Building Blocks (oneTBB) in your Build Environment Define the TBBROOT Environment Variable Enable C++11 Lambda Expression Support with Intel® oneAPI Threading Building Blocks (oneTBB) Enable OpenMP* in your Build Environment
Annotation Report x
Annotation Report Overview Locate Annotations with the Annotation Report
Replace Annotations with Intel® oneAPI Threading Building Blocks (oneTBB) Code x
Intel® oneAPI Threading Building Blocks (oneTBB) Mutexes Intel® oneAPI Threading Building Blocks (oneTBB) Simple Mutex - Example Test the Intel® oneAPI Threading Building Blocks (oneTBB) Synchronization Code Parallelize Functions - Intel® oneAPI Threading Building Blocks (oneTBB) Tasks Parallelize Data - Intel® oneAPI Threading Building Blocks (oneTBB) Counted Loops Parallelize Data - Intel® oneAPI Threading Building Blocks (oneTBB) Loops with Complex Iteration Control
Replace Annotations with OpenMP* Code x
Add OpenMP Code to Synchronize the Shared Resources OpenMP Critical Sections Basic OpenMP Atomic Operations Advanced OpenMP Atomic Operations OpenMP Reduction Operations OpenMP Locks Test the OpenMP Synchronization Code Parallelize Functions - OpenMP Tasks Parallelize Data - OpenMP Counted Loops Parallelize Data - OpenMP Loops with Complex Iteration Control
Next Steps for the Parallel Program x
Use Intel® Inspector and Intel® VTune™ Profiler Debug Parallel Programs
Model Offloading to a GPU x
Run Offload Modeling Perspective from GUI Run Offload Modeling Perspective from Command Line Explore Offload Modeling Results Advanced Modeling Configuration
Run Offload Modeling Perspective from GUI x
Offload Modeling Accuracy Presets Customize Offload Modeling Perspective
Run Offload Modeling Perspective from Command Line x
Offload Modeling Accuracy Levels in Command Line Run GPU-to-GPU Performance Modeling from Command Line
Explore Offload Modeling Results x
Offload Modeling Report Overview Examine Regions Recommended for Offloading Examine Data Transfers for Modeled Regions Check for Dependency Issues Explore Performance Gain from GPU-to-GPU Modeling Investigate Non-Offloaded Code Regions
Advanced Modeling Configuration x
Model Application Performance on a Custom Target GPU Device Check How Assumed Dependencies Affect Modeling Manage Invocation Taxes Enforce Offloading for Specific Loops
Analyze GPU Roofline x
Test Topic for Embedded Help Run GPU Roofline Insights Perspective from GUI Run GPU Roofline Insights Perspective from Command Line Explore GPU Roofline Results
Run GPU Roofline Insights Perspective from GUI x
GPU Roofline Accuracy Presets Customize GPU Roofline Insights Perspective
Run GPU Roofline Insights Perspective from Command Line x
GPU Roofline Accuracy Levels in Command Line
Explore GPU Roofline Results x
Examine GPU Roofline Summary Examine Bottlenecks on GPU Roofline Chart Examine Kernel Details Compare GPU Roofline Results
Design and Analyze Flow Graphs x
Where to Find the Flow Graph Analyzer Launching the Flow Graph Analyzer Flow Graph Analyzer GUI Overview Flow Graph Analyzer Workflows Designer Workflow Generating C++ Stubs Preferences Scalability Analysis Collecting Traces from Applications Nested Parallelism in Flow Graph Analyzer Analyzer Workflow Experimental Support for OpenMP* Applications Sample Trace Files Additional Resources
Flow Graph Analyzer GUI Overview x
Menus Toolbars Tabs Main Canvas Charts
Designer Workflow x
Adding Nodes, Edges, and Ports Modifying Node Properties Viewing Edge Properties Validating a Graph Saving a Graph to a File
Scalability Analysis x
Activating the Graph Scalability Analysis Prerequisites Running the Scalability Analysis Exploring the Parallelism in a Concurrent Node Showing Non-Parallel Nature of a Serial Node Explore Parallelism Provided by the Topology of a Graph Understanding Analysis Color Codes
Scalability Analysis Prerequisites x
Setting Concurrency Specification Setting Data Count Setting Node Weight
Collecting Traces from Applications x
Building an Application for Trace Collection Collecting Trace Files
Building an Application for Trace Collection x
Building an Application on Windows* OS Building an Application on Linux* OS Building an Application on macOS*
Collecting Trace Files x
Collect Traces In the Flow Graph Analyzer GUI Collect Traces Outside the Flow Graph Analyzer GUI
Collect Traces Outside the Flow Graph Analyzer GUI x
Collecting Trace Files with fgtrun Script Collecting Trace Files without fgtrun Script
Analyzer Workflow x
Find Time Regions of Low Concurrency and Their Cause Finding a Critical Path Finding Tasks with Small Durations Reduce Scheduler Overhead using Lightweight Policy Identifying Tasks that Operate on Common Input Support for SYCL
Support for SYCL x
Collect SYCL Application Traces Examine a SYCL Application Graph Find Issues Using Static Rule-check Engine
Examine a SYCL Application Graph x
Hotspot View View Performance Inefficiencies of Data-parallel Constructs
Find Issues Using Static Rule-check Engine x
Issue: Const Reference to a Host Pointer Used to Initialize a Buffer Issue: Host Pointer Accessor Used in a Loop Issue: Data Parallel Construct Inefficiency
Experimental Support for OpenMP* Applications x
Collecting Traces for OpenMP* Applications OpenMP* Constructs in the Per-Thread Task View OpenMP* Constructs in the Graph Canvas
Sample Trace Files x
code_generation Samples performance_analysis Samples
Minimize Analysis Overhead x
Collection Controls to Minimize Analysis Overhead Loop Markup to Minimize Analysis Overhead Filtering to Minimize Analysis Overhead Execution Speed/Duration/Scope Properties to Minimize Analysis Overhead Miscellaneous Techniques to Minimize Analysis Overhead
Analyze MPI Applications x
Model MPI Application Performance on GPU Control Collection with an MPI_Pcontrol Function
Manage Results x
Open a Result Rename an Existing Result Delete a Result Save Results to a Custom Location Work with Standalone HTML Reports Create a Read-only Result Snapshot Create a Result Snapshot Dialog Box
Command Line Interface x
advisor Command Line Interface Reference Offload Modeling Command Line Reference Generate Pre-configured Command Lines
advisor Command Line Interface Reference x
advisor Command Action Reference advisor Command Option Reference
advisor Command Action Reference x
collect command create-project help import-dir mark-up-loops report snapshot version workflow
advisor Command Option Reference x
accuracy append app-working-dir assume-dependencies assume-hide-taxes assume-ndim-dependency assume-single-data-transfer auto-finalize batching benchmarks-sync bottom-up cache-binaries cache-binaries-mode cache-config cache-simulation cache-sources cachesim cachesim-associativity cachesim-cacheline-size cachesim-mode cachesim-sampling-factor cachesim-sets check-profitability clear config count-logical-instructions count-memory-instructions count-memory-objects-accesses count-mov-instructions count-send-latency cpu-scale-factor csv-delimiter custom-config data-limit data-reuse-analysis data-transfer data-transfer-histogram data-transfer-page-size data-type delete-tripcounts disable-fp64-math-optimization display-callstack dry-run duration dynamic enable-cache-simulation enable-data-transfer-analysis enable-task-chunking enforce-baseline-decomposition enforce-fallback enforce-offloads estimate-max-speedup evaluate-min-speedup exclude-files executable-of-interest exp-dir filter filter-by-scope filter-reductions flop force-32bit-arithmetics force-64bit-arithmetics format gpu gpu-carm gpu-kernel-of-interest gpu-sampling-interval hide-data-transfer-tax ignore ignore-app-mismatch ignore-checksums instance-of-interest integrated interval limit loop-call-count-limit loop-filter-threshold loops mark-up mark-up-list memory-level memory-operation-type mix mkl-user-mode model-baseline-gpu model-children model-extended-math model-system-calls module-filter module-filter-mode mpi-rank mrte-mode ndim-depth-limit option-file overlap-taxes pack profile-gpu profile-intel-perf-libs profile-jit profile-python profile-stripped-binaries project-dir quiet recalculate-time record-mem-allocations record-stack-frame reduce-lock-contention reduce-lock-overhead reduce-site-overhead reduce-task-overhead refinalize-survey remove report-output report-template result-dir resume-after return-app-exitcode search-dir search-n-dim select set-dependency set-parallel set-parameter show-all-columns show-all-rows show-functions show-loops show-not-executed show-report small-node-filter sort-asc sort-desc spill-analysis stack-access-granularity stack-stitching stack-unwind-limit stacks stackwalk-mode start-paused static-instruction-mix strategy support-multi-isa-binaries target-device target-gpu target-pid target-process target-system threading-model threads top-down trace-mode trace-mpi track-memory-objects track-stack-accesses track-stack-variables trip-counts verbose with-stack
Offload Modeling Command Line Reference x
run_oa.py Options collect.py Options analyze.py Options
Troubleshooting x
Error Message: Application Sets Its Own Handler for Signal Error Message: Cannot Collect GPU Hardware Metrics for the Selected GPU Adapter Error Message: Memory Model Cache Hierarchy Incompatible Error Message: No Annotations Found Error Message: No Data Is Collected Error Message: Stack Size Is Too Small Error Message: Undefined Linker References to dlopen or dlsym Problem: Broken Call Tree Problem: Code Region is not Marked Up Problem: Debug Information Not Available Problem: No Data Problem: Source Not Available Problem: Stack in the Top-Down Tree Window Is Incorrect Problem: Survey Tool does not Display Survey Report Problem: Unexpected C/C++ Compilation Errors After Adding Annotations Problem: Unexpected Unmatched Annotations in the Dependencies Report Warning: Analysis of Debug Build Warning: Analysis of Release Build
Reference x
Data Reference Dependencies Problem and Message Types Recommendation Reference User Interface Reference
Data Reference x
CPU Metrics Accelerator Metrics
Dependencies Problem and Message Types x
Dangling Lock Data Communication Data Communication, Child Task Inconsistent Lock Use Lock Hierarchy Violation Memory Reuse Memory Reuse, Child Task Memory Watch Missing End Site Missing End Task Missing Start Site Missing Start Task No Tasks in Parallel Site One Task Instance in Parallel Site Orphaned Task Parallel Site Information Thread Information Unhandled Application Exception
Recommendation Reference x
Vectorization Recommendations for C++ Vectorization Recommendations for Fortran
User Interface Reference x
Dialog Box: Corresponding Command Line Dialog Box: Create a Project Dialog Box: Create a Result Snapshot Dialog Box: Options - Assembly Editor Tab Dialog Box: Options - General Dialog Box: Options - Result Location Dialog Box: Project Properties - Analysis Target Dialog Box: Project Properties - Binary/Symbol Search Dialog Box: Project Properties - Source Search Pane: Advanced View Pane: Analysis Workflow Pane: Roofline Chart Pane: GPU Roofline Chart Project Navigator Pane Toolbar: Intel Advisor Annotation Report Window: Dependencies Source Window: GPU Roofline Regions Window: GPU Roofline Insights Summary Window: Memory Access Patterns Source Window: Offload Modeling Summary Window: Offload Modeling Report - Accelerated Regions Window: Perspective Selector Window: Refinement Reports Window: Suitability Report Window: Suitability Source Window: Survey Report Window: Survey Source Window: Threading Summary Window: Vectorization Summary
Window: Refinement Reports x
Tab: Dependencies Report Tab: Memory Access Patterns Report
Appendix x
Data Sharing Problems Notational Conventions Key Concepts Related Information
Data Sharing Problems x
Data Sharing Problem Types Problem Solving Strategies
Data Sharing Problem Types x
Incidental Sharing Independent Updates
Problem Solving Strategies x
Eliminate Incidental Sharing Synchronize Independent Updates Difficult Problems: Choosing a Different Set of Tasks Fix Problems in Code Used by Multiple Parallel Sites Memory That is Accessed Through a Pointer
Eliminate Incidental Sharing x
Examine the Task's Static and Dynamic Extent Verify Whether Incidental Sharing Exists Create the Private Memory Location Pointer Dereferences
Synchronize Independent Updates x
Synchronization Explicit Locking Assign Locks to Transactions Pitfalls from Using Synchronization
Key Concepts x
Glossary Parallelism
Parallelism x
Parallel Processing Terminology Add Parallelism Common Issues When Adding Parallelism Parallel Programming Implementations
Intel® Advisor User Guide

Glossary

Amdahl's law: A theoretical formula for predicting the maximum performance benefits of parallelizing application programs. Amdahl's law states that run-time execution time speedup is limited by the part of the program that is not parallelized (executes serially). To achieve results close to this potential, overhead must be minimized and all cores need to be fully utilized. See also Use Amdahl's Law and Measuring the Program.

annotation: A method of conveying information about proposed parallel execution. In the Intel® Advisor, you create annotations by adding macros or function calls. These annotations are used by Intel Advisor tools to predict parallel execution. For example, the C/C++ ANNOTATE_SITE_BEGIN(sitename) macro identifies where a parallel site begins. Later, to allow this code to execute in parallel, you replace the annotations with code needed to use a parallel framework. See also parallel framework and Annotation Types Summary.

atomic operation: An operation performed by a thread on a memory location(s) that is guaranteed not to be interfered with by other threads. See also synchronization.

chunking: The ability of a parallel framework to aggregate multiple instances of a task into groups for more efficient parallel processing. For tasks that do small amounts of computation and many iterations, task chunking can minimize task overhead. You can also restructure a single loop into an inner and outer loop (strip-mining). See also task and Enable Task Chunking.

code region: A subtree of loops/functions in a call tree. Synonym whole Loopnest.

critical section: A synchronization construct that allows only one thread to enter its associated code region at a time. Critical sections enforce mutual exclusion on enclosed regions of code. With Intel Advisor, mark critical sections by using ANNOTATE_LOCK_ACQUIRE() and ANNOTATE_LOCK_RELEASE() annotations.

data race: When multiple threads share (read/write) a memory location, if the program does not implement controls to manage the sequence of concurrent memory accesses, one thread can inadvertently overwrite data written by another thread, or otherwise read or write stale data. This can produce execution errors that are difficult to detect and reproduce, such as obtaining different calculated results when the same  executable is run on different systems. To prevent data races, you can add data synchronization constructs that restrict shared memory access to one thread at a time, or you might eliminate the sharing.

data parallelism: Occurs when a single portion of code is paired with multiple portions of  data, and each pairing executes as a task. For example, tasks are made by pairing a loop body with each element of an array iterated by the loop, and the tasks execute in parallel. See also Task Patterns. Contrast task parallelism.

data set: A set of data to be used as input or with an interactive application the way you interact with the application to cause a portion of the application to be executed. Because the Dependencies tool watches each memory access in a parallel site in great detail, the parallel site's code takes much longer to run than usual. To limit the time needed to run Dependencies analysis, reduce the data (such as the number of loop iterations) and when using an interactive program, create a very small test case. See also Choose a Small, Representable Data Set for the Dependencies Tool.

deadlock: A situation where a set of threads have each acquired some locks and are waiting for other locks to be released. All threads in the set are waiting for a lock held by a different thread, and since none can proceed and release their lock(s), they all remain waiting.

dynamic extent: All code that may possibly be executed by a parallel site or task. For example, a dynamic extent might include a loop, all functions called from the loop, all functions the called functions may in turn call, and so on. Contrast static extent. See also Task Organization and Annotations.

false positive: When viewing the Dependencies Report, a problem reported by the Dependencies tool that is not an actual problem.

framework: See parallel framework

head: A loop or function at the top of a subtree, which contains one or more child loops/functions.

hotspot: A small code region that consumes much of the program's run time. Hotspots can be identified by a profiler, such as the Intel Advisor Survey tool. See also Use Amdahl's Law and Measuring the Program.

Intel® oneAPI Threading Building Blocks (oneTBB) : A C++ template library for writing programs that take advantage of multiple cores. You can use this library to write scalable programs that specify tasks rather than threads, emphasize data parallel programming, and take advantage of concurrent collections and parallel algorithms. This is provided as an Intel® software product - Intel® oneAPI Threading Building Blocks (oneTBB) - as well as open source. Intel® oneAPI Threading Building Blocks (oneTBB) is one of several parallel frameworks. Abbreviation oneTBB .

load balancing: The equal division of work among cores. If the load is balanced, the cores are busy most of the time.

lock: A synchronization mechanism that allows one thread to wait until another thread allows it to continue. A lock can be used to synchronize threads accessing a specific memory location. See also synchronization and nested lock.

multi-core: A processor that combines two or more independent cores. Although each core shares interconnection to the rest of the system, it executes instructions independently by using its dedicated CPU, architectural state, and interrupt controllers, as well as private and/or shared cache. Most multi-core systems use identical cores. The number of cores used determines whether it is called dual-core (2), quad-core (4), or many-core system.

multithreaded processing: See parallel processing

mutual exclusion: A type of locking typically used to prevent actions occurring at the same time. Abbreviation mutex. See also synchronization

nested lock: A type of lock that can be locked again by a task when the task already owns the lock. Nested locks are convenient when several inter-related functions use the same lock. See also synchronization and lock

node: A loop or function.

oneTBB : See IIntel® oneAPI Threading Building Blocks (oneTBB)

OpenMP*: A high-level parallel framework and language extension designed to support shared-memory parallel programming that consists of compiler directives (C/C++ pragmas and Fortran directives), library functions, and environment variables. The OpenMP specification was developed by multiple hardware and software vendors to provide a scalable, portable interface for parallel programming on a variety of platforms. OpenMP is one of several parallel frameworks. See also http://openmp.org.

parallel framework: A combination of libraries, language features, or other software techniques that enable code for a program to execute in parallel. Examples include OpenMP, Intel® oneAPI Threading Building Blocks (oneTBB) , Message Passing Interface (MPI), Intel® Concurrent Collections for C/C++, Microsoft Task Parallel Library* (TPL), and low-level, basic threading APIs, like POSIX* threads (Pthreads). Some parallel frameworks support shared-memory parallel processing, while others like MPI support non-shared-memory parallel processing. See also Intel® oneAPI Threading Building Blocks (oneTBB) and Parallel Frameworks Overview.

parallel processing: The use of multiple threads during execution of a program. Intel Advisor focuses on parallel processing for shared-memory systems. There are other types of parallel processing, such as for clusters or grids and vector processing. Shortened version is parallelism. See also hotspot and thread.

parallel region:Offload Modeling term. A code region that starts with a specific parallel framework construction. Intel® oneAPI Threading Building Blocks (oneTBB),Intel® oneAPI Data Analytics Library (oneDAL), OpenMP*, SYCL parallel frameworks are supported.

parallel site: A region of code that contains tasks that can execute in parallel. See also annotation and Task Organization and Annotations

pipeline: An approach to organizing task computations that uses both data parallelism and task parallelism, and organizes the computation into stages that run in a predetermined order.

self time: In the Survey Report window, how much time was spent in a particular function or loop.

site: See parallel site

shared-memory parallelism: See parallel processing

static extent: The code between a site's or a task's _BEGIN and _END annotations. A static extent might not be lexically paired; for example, a parallel site may have one _BEGIN point, but may require multiple independent _END exit points. Contrast with dynamic extent. See also annotation, parallel site, and Task Organization and Annotations.

synchronization: Coordinating the execution of multiple threads. In some cases, you can provide synchronization within a task by using a private memory location instead of a shared memory location. In other cases, a lock or mutex can be used to restrict access to a shared data. See also Data Sharing Problem Types.

task: A portion of code and its data that can be given to a thread to execute. See also Task Organization and Annotations, Choosing the Tasks, and chunking.

task parallelism: Occurs when two different portions of the code are made into tasks and execute in parallel. For example, a task is made by pairing a display algorithm with the state to display, another task by pairing a compute-next-state algorithm with the same state, and the two tasks execute in parallel. See also Task Patterns. Contrast data parallelism

thread: A thread executes instructions within a process. Each process has one or more threads active at a time. Threads share the address space of the process, but have their own stack, program counters, and other registers.

total time: In the Survey Report window, how much time was spent in a particular function or loop, plus the time spent by anything that entity calls.

vector processing: A form of parallel processing where multiple data items are packed together in vector registers to allow vector instructions to operate on the packed data with a single instruction. Reducing the number of instructions needed to process the packed vector data minimizes memory use and latency, and provides good locality of reference and data cache utilization. Vector instructions are Single Instruction Multiple Data (SIMD) instructions. Some SIMD vector instructions support large register sizes to accommodate more packed data, such as Intel® Advanced Vector Extensions (Intel® AVX).

Parent topic: Key Concepts
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