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neon60 committed Jun 27, 2024
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1 change: 1 addition & 0 deletions .wordlist.txt
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NCCL
NDRange
nonnegative
NOP
Numa
Nsight
overindex
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189 changes: 68 additions & 121 deletions docs/how-to/unified_memory.rst
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Unified Memory
*******************************************************************************

Introduction
============
In conventional architectures, CPUs and GPUs have dedicated memory like Random
Access Memory (RAM) and Video Random Access Memory (VRAM). This architectural
design, while effective, can be limiting in terms of memory capacity and
Expand All @@ -35,7 +33,7 @@ throughput (data processed by unit time).

.. _unified memory system requirements:

System Requirements
System requirements
===================
Unified memory is supported on Linux by all modern AMD GPUs from the Vega
series onward. Unified memory management can be achieved with managed memory
Expand Down Expand Up @@ -75,38 +73,43 @@ the next section.
❌: **Unsupported**

:sup:`1` Works only with ``XNACK=1``. First GPU access causes recoverable
page-fault.
page-fault. For more details, visit
`GPU memory <https://rocm.docs.amd.com/en/latest/conceptual/gpu-memory.html#xnack>`_.

.. _unified memory programming models:

Unified Memory Programming Models
Unified memory programming models
=================================

Showcasing various unified memory programming models, their availability
depends on your architecture. For further details, visit :ref:`unified memory
Showcasing various unified memory programming models, the model availability
depends on your architecture. For more information, see :ref:`unified memory
system requirements` and :ref:`checking unified memory management support`.

- **HIP Managed Memory Allocation API**:
The ``hipMallocManaged()`` is a dynamic memory allocator that is available on
all GPUs with unified memory support. For more details, visit :doc:`reference
page <reference/unified_memory_reference>`.
- **HIP managed memory allocation API**:

- **HIP Managed Variables**:
The ``__managed__`` declaration specifier, which serves as its counterpart, is
supported on all modern AMD cards and can be utilized for static allocation.
The ``hipMallocManaged()`` is a dynamic memory allocator available on
all GPUs with unified memory support. For more details, visit
:ref:`unified_memory_reference`.

- **System Allocation API**:
Starting with the MI300 series, the ``malloc()`` system allocator allows you
to reserve unified memory. The system allocator is more versatile, and it
offers an easy transition from a CPU written C++ code to a HIP code as the same
system allocation API is used.
- **HIP managed variables**:

The ``__managed__`` declaration specifier, which serves as its counterpart,
is supported on all modern AMD cards and can be utilized for static
allocation.

- **System allocation API**:

Starting with the AMD MI300 series, the ``malloc()`` system allocator allows
you to reserve unified memory. The system allocator is more versatile and
offers an easy transition from a CPU written C++ code to a HIP code as the
same system allocation API is used.

.. _checking unified memory management support:

Checking Unified Memory Management Support
Checking unified memory management support
------------------------------------------
Some device attribute can offer information about which :ref:`unified memory
programming models` are supported. The attribute value is an integer 1 if the
Some device attributes can offer information about which :ref:`unified memory
programming models` are supported. The attribute value is 1 if the
functionality is supported, and 0 if it is not supported.

.. list-table:: Device attributes for unified memory management
Expand Down Expand Up @@ -142,7 +145,7 @@ The following examples show how to use device attributes:
return 0;
}
Example for Unified Memory Management
Example for unified memory management
-------------------------------------

The following example shows how to use unified memory management with
Expand Down Expand Up @@ -321,46 +324,50 @@ Memory Management example is presented in the last tab.
.. _using unified memory management:

Using Unified Memory Management (UMM)
Using unified memory management (UMM)
=====================================
Unified Memory Management (UMM) is a feature that can simplify the complexities

Unified memory management (UMM) is a feature that can simplify the complexities
of memory management in GPU computing. It is particularly useful in
heterogeneous computing environments with heavy memory usage with both a CPU
and a GPU, which would require large memory transfers. Here are some areas
where UMM can be beneficial:

- **Simplification of Memory Management**:
UMM can help to simplify the complexities of memory management. This can make
it easier for developers to write code without worrying about memory allocation
and deallocation details.

UMM can help to simplify the complexities of memory management. This can make
it easier for developers to write code without worrying about memory
allocation and deallocation details.

- **Data Migration**:
UMM allows for efficient data migration between the host (CPU) and the device
(GPU). This can be particularly useful for applications that need to move data
back and forth between the device and host.

UMM allows for efficient data migration between the host (CPU) and the device
(GPU). This can be particularly useful for applications that need to move
data back and forth between the device and host.

- **Improved Programming Productivity**:
As a positive side effect, the use of UMM can reduce the lines of code,
thereby improving programming productivity.

As a positive side effect, UMM can reduce the lines of code, thereby
improving programming productivity.

In HIP, pinned memory allocations are coherent by default. Pinned memory is
host memory mapped into the address space of all GPUs, meaning that the pointer
can be used on both host and device. Using pinned memory instead of pageable
memory on the host can improve bandwidth.

While UMM can provide numerous benefits, it is important to be aware of the
While UMM can provide numerous benefits, it's important to be aware of the
potential performance overhead associated with UMM. You must thoroughly test
and profile your code to ensure it is the most suitable choice for your use
and profile your code to ensure it's the most suitable choice for your use
case.

.. _unified memory compiler hints:
.. _unified memory runtime hints:

Unified Memory Compiler Hints for the Better Performance
========================================================
Unified memory HIP runtime hints for the better performance
===========================================================

Unified memory compiler hints can help to improve the performance of your code,
if you know the ability of your code and the infrastructure that you use. Some
hint techniques are presented in this section.
Unified memory HIP runtime hints can help improve the performance of your code if
you know your code's ability and infrastructure. Some hint techniques are
presented in this section.

The hint functions can set actions on a selected device, which can be
identified by ``hipGetDeviceProperties(&prop, device_id)``. There are two
Expand All @@ -369,13 +376,14 @@ special ``device_id`` values:
- ``hipCpuDeviceId`` = -1 means that the advised device is the CPU.
- ``hipInvalidDeviceId`` = -2 means that the device is invalid.

For the best performance you can profile your application to optimize the
utilization of compiler hits.
For the best performance, profile your application to optimize the
utilization of HIP runtime hints.

Data Prefetching
Data prefetching
----------------

Data prefetching is a technique used to improve the performance of your
application by moving data closer to the processing unit before it is actually
application by moving data closer to the processing unit before it's actually
needed.

.. code-block:: cpp
Expand Down Expand Up @@ -429,10 +437,11 @@ needed.
}
Remember to check the return status of ``hipMemPrefetchAsync()`` to ensure that
the prefetch operations complete successfully!
the prefetch operations are completed successfully.

Memory Advise
Memory advice
-------------

The effectiveness of ``hipMemAdvise()`` comes from its ability to inform the
runtime system of the developer's intentions regarding memory usage. When the
runtime system has knowledge of the expected memory access patterns, it can
Expand All @@ -441,10 +450,10 @@ efficient execution of the application. However, the actual impact on
performance can vary based on the specific use case and the hardware
architecture.

For the description of ``hipMemAdvise()`` and the detailed list of advises,
visit the :doc:`reference page <reference/unified_memory_reference>`.
For the description of ``hipMemAdvise()`` and the detailed list of advice,
visit the :ref:`unified_memory_reference`.

Here is the updated version of the example above with memory advises.
Here is the updated version of the example above with memory advice.

.. code-block:: cpp
:emphasize-lines: 17-26
Expand All @@ -460,17 +469,17 @@ Here is the updated version of the example above with memory advises.
int main() {
int *a, *b, *c;
// Allocate memory for a, b and c that is accessible to both device and host codes.
// Allocate memory for a, b, and c accessible to both device and host codes.
hipMallocManaged(&a, sizeof(*a));
hipMallocManaged(&b, sizeof(*b));
hipMallocManaged(&c, sizeof(*c));
// Set memory advise for a, b, and c to be accessed by the CPU.
// Set memory advice for a, b, and c to be accessed by the CPU.
hipMemAdvise(a, sizeof(*a), hipMemAdviseSetPreferredLocation, hipCpuDeviceId);
hipMemAdvise(b, sizeof(*b), hipMemAdviseSetPreferredLocation, hipCpuDeviceId);
hipMemAdvise(c, sizeof(*c), hipMemAdviseSetPreferredLocation, hipCpuDeviceId);
// Additionally, set memory advise for a, b, and c to be read mostly from the device 0.
// Additionally, set memory advice for a, b, and c to be read mostly from the device 0.
constexpr int device = 0;
hipMemAdvise(a, sizeof(*a), hipMemAdviseSetReadMostly, device);
hipMemAdvise(b, sizeof(*b), hipMemAdviseSetReadMostly, device);
Expand Down Expand Up @@ -498,16 +507,17 @@ Here is the updated version of the example above with memory advises.
}
Memory Range attributes
Memory range attributes
-----------------------

Memory Range attributes allow you to query attributes of a given memory range.

The ``hipMemRangeGetAttribute()`` is added to the example to query the
``hipMemRangeAttributeReadMostly`` attribute of the memory range pointed to by
``a``. The result is stored in ``attributeValue`` and then printed out.

For more details, visit the
:doc:`reference page <reference/unified_memory_reference>`.
:ref:`unified_memory_reference`.

.. code-block:: cpp
:emphasize-lines: 29-34
Expand Down Expand Up @@ -559,72 +569,9 @@ For more details, visit the
return 0;
}
Asynchronously Attach Memory to a Stream
Asynchronously attach memory to a stream
----------------------------------------

The ``hipStreamAttachMemAsync`` function is used to asynchronously attach
memory to a stream, which can help with concurrent execution when using
streams.

In the example, a stream is created by using ``hipStreamCreate()`` and then
the managed memory is attached to the stream using
``hipStreamAttachMemAsync()``. The ``hipMemAttachGlobal`` flag is used to
indicate that the memory can be accessed from any stream on any device.
The kernel launch and synchronization are now done on this stream.
Using streams and attaching memory to them can help with overlapping data
transfers and computation.

For more details and description of flags, visit
:doc:`reference page <reference/unified_memory_reference>`.

.. code-block:: cpp
:emphasize-lines: 21-24
#include <hip/hip_runtime.h>
#include <iostream>
// Addition of two values.
__global__ void add(int *a, int *b, int *c) {
*c = *a + *b;
}
int main() {
int *a, *b, *c;
hipStream_t stream;
// Create a stream.
hipStreamCreate(&stream);
// Allocate memory for a, b and c that is accessible to both device and host codes.
hipMallocManaged(&a, sizeof(*a));
hipMallocManaged(&b, sizeof(*b));
hipMallocManaged(&c, sizeof(*c));
// Attach memory to the stream asynchronously.
hipStreamAttachMemAsync(stream, a, sizeof(*a), hipMemAttachGlobal);
hipStreamAttachMemAsync(stream, b, sizeof(*b), hipMemAttachGlobal);
hipStreamAttachMemAsync(stream, c, sizeof(*c), hipMemAttachGlobal);
// Setup input values.
*a = 1;
*b = 2;
// Launch add() kernel on GPU on the created stream.
hipLaunchKernelGGL(add, dim3(1), dim3(1), 0, stream, a, b, c);
The ``hipStreamAttachMemAsync`` function would be able to asynchronously attach memory to a stream, which can help concurrent execution when using streams.

// Wait for stream to finish before accessing on host.
hipStreamSynchronize(stream);
// Prints the result.
std::cout << *a << " + " << *b << " = " << *c << std::endl;
// Cleanup allocated memory.
hipFree(a);
hipFree(b);
hipFree(c);
// Destroy the stream.
hipStreamDestroy(stream);
return 0;
}
Currently, this function is a no-operation (NOP) function on AMD GPUs. It simply returns success after the runtime memory validation passed. This function is necessary on Microsoft Windows, and UMM is not supported on this operating system with AMD GPUs at the moment.
2 changes: 1 addition & 1 deletion docs/index.md
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Expand Up @@ -40,7 +40,7 @@ The CUDA enabled NVIDIA GPUs are supported by HIP. For more information, see [GP
* {doc}`./how-to/performance_guidelines`
* [Debugging with HIP](./how-to/debugging)
* {doc}`./how-to/logging`
* [Unified Memory](./reference/unified_memory)
* [Unified Memory](./how-to/unified_memory)
* {doc}`./how-to/faq`

:::
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6 changes: 2 additions & 4 deletions docs/reference/unified_memory_reference.rst
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Expand Up @@ -3,13 +3,11 @@
how to use it in AMD HIP.
:keywords: AMD, ROCm, HIP, CUDA, unified memory, unified, memory, UM, APU

.. _unified_memory_reference:

*******************************************************************************
HIP Managed Memory Allocation API
*******************************************************************************

.. doxygengroup:: MemoryM
:content-only:

.. doxygenfunction:: hipMemoryAdvise

.. doxygenfunction:: hipMemRangeAttribute

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