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Execution Model Overview
Thread Mapping and GPU Occupancy
Kernels
Using Libraries for GPU Offload
Host/Device Memory, Buffer and USM
Unified Shared Memory Allocations
Performance Impact of USM and Buffers
Avoiding Moving Data Back and Forth between Host and Device
Optimizing Data Transfers
Avoiding Declaring Buffers in a Loop
Buffer Accessor Modes
Host/Device Coordination
Using Multiple Heterogeneous Devices
Compilation
OpenMP Offloading Tuning Guide
Multi-GPU and Multi-Stack Architecture and Programming
Level Zero
Performance Profiling and Analysis
Configuring GPU Device
Sub-Groups and SIMD Vectorization
Removing Conditional Checks
Registers and Performance
Shared Local Memory
Pointer Aliasing and the Restrict Directive
Synchronization among Threads in a Kernel
Considerations for Selecting Work-Group Size
Prefetch
Reduction
Kernel Launch
Executing Multiple Kernels on the Device at the Same Time
Submitting Kernels to Multiple Queues
Avoiding Redundant Queue Constructions
Programming Intel® XMX Using SYCL Joint Matrix Extension
Doing I/O in the Kernel
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Using Intel® MPI Library
GPUs have become common in HPC clusters and the Intel® MPI Library now supports scale-up and scale-out executions of large applications on such heterogeneous machines.
GPU support was first introduced in Intel® MPI Library 2019 U8 (Intel® MPI Library Release Notes). Several enhancements have since been added in this direction. Unless otherwise stated, all the tests in this section used Intel® MPI Library 2021.8.
- Running MPI Applications on GPUs
- Intra-Device and Inter-Device Data Transfers for MPI+OpenMP Programs