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
Optimizing Explicit SIMD Kernels
FLAT Mode Programming
As mentioned previously, the FLAT mode is the default mode on Intel® Data Center GPU Max Series. In FLAT mode, each stack is exposed as a root device. In this section, we present SYCL and OpenMP examples to demonstrate offloading in FLAT mode.
Memory in FLAT Mode
Each stack has its own memory. A kernel offloaded to a stack will run on that stack and use the memory allocated on that stack.
A kernel running on a stack can access memory on other stacks in the same GPU card. However, accessing memory on a stack other than the stack it is running on will be slower.