Istvan Kiss
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Change-Id: I179814351b77935aff55e8ae47dd322a3e15a868
[ROCm/hip commit: f39c7a3150]
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83 строки
4.0 KiB
ReStructuredText
.. meta::
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:description: HIP programming guide introduction
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:keywords: HIP programming guide introduction, HIP programming guide
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.. _hip-programming-guide:
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********************************************************************************
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HIP programming guide introduction
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********************************************************************************
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This topic provides key HIP programming concepts and links to more detailed
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information.
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Write GPU Kernels for Parallel Execution
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================================================================================
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To make the most of the parallelism inherent to GPUs, a thorough understanding
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of the :ref:`programming model <programming_model>` is helpful. The HIP
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programming model is designed to make it easy to map data-parallel algorithms to
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architecture of the GPUs. HIP employs the SIMT-model (Single Instruction
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Multiple Threads) with a multi-layered thread hierarchy for efficient execution.
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Understand the Target Architecture (CPU and GPU)
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================================================================================
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The :ref:`hardware implementation <hardware_implementation>` topic outlines the
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GPUs supported by HIP. In general, GPUs are made up of Compute Units that excel
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at executing parallelizable, computationally intensive workloads without complex
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control-flow.
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Increase parallelism on multiple level
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================================================================================
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To maximize performance and keep all system components fully utilized, the
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application should expose and efficiently manage as much parallelism as possible.
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:ref:`Parallel execution <parallel execution>` can be achieved at the
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application, device, and multiprocessor levels.
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The application’s host and device operations can achieve parallel execution
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through asynchronous calls, streams, or HIP graphs. On the device level,
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multiple kernels can execute concurrently when resources are available, and at
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the multiprocessor level, developers can overlap data transfers with
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computations to further optimize performance.
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Memory management
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================================================================================
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GPUs generally have their own distinct memory, also called :ref:`device
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memory <device_memory>`, separate from the :ref:`host memory <host_memory>`.
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Device memory needs to be managed separately from the host memory. This includes
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allocating the memory and transfering it between the host and the device. These
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operations can be performance critical, so it's important to know how to use
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them effectively. For more information, see :ref:`Memory management <memory_management>`.
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Synchronize CPU and GPU Workloads
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================================================================================
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Tasks on the host and devices run asynchronously, so proper synchronization is
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needed when dependencies between those tasks exist. The asynchronous execution
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of tasks is useful for fully utilizing the available resources. Even when only a
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single device is available, memory transfers and the execution of tasks can be
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overlapped with asynchronous execution.
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Error Handling
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================================================================================
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All functions in the HIP runtime API return an error value of type
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:cpp:enum:`hipError_t` that can be used to verify whether the function was
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successfully executed. It's important to confirm these returned values, in order
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to catch and handle those errors, if possible. An exception is kernel launches,
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which don't return any value. These errors can be caught with specific functions
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like :cpp:func:`hipGetLastError()`.
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For more information, see :ref:`error_handling` .
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Multi-GPU and Load Balancing
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================================================================================
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Large-scale applications that need more compute power can use multiple GPUs in
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the system. This requires distributing workloads across multiple GPUs to balance
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the load to prevent GPUs from being overutilized while others are idle.
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For more information, see :ref:`multi-device` . |