Heterogeneous System Architecture#HMM

The rationale behind HSA is to ease the burden on programmers when offloading calculations to the GPU. Originally driven solely by AMD and called the FSA, the idea was extended to encompass processing units other than GPUs, such as other manufacturers' DSPs, as well.

{{Gallery

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| align = center

| File:HSA – using the GPU without HSA.svg

| Steps performed when offloading calculations to the GPU on a non-HSA system

|File:HSA – using the GPU with HSA.svg

| Steps performed when offloading calculations to the GPU on a HSA system, using the HSA functionality

}}

Modern GPUs are very well suited to perform single instruction, multiple data (SIMD) and single instruction, multiple threads (SIMT), while modern CPUs are still being optimized for branching. etc.

{{More citations needed section|date=May 2014}}

Originally introduced by embedded systems such as the Cell Broadband Engine, sharing system memory directly between multiple system actors makes heterogeneous computing more mainstream. Heterogeneous computing itself refers to systems that contain multiple processing units{{snd}} central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), or any type of application-specific integrated circuits (ASICs). The system architecture allows any accelerator, for instance a graphics processor, to operate at the same processing level as the system's CPU.

Among its main features, HSA defines a unified virtual address space for compute devices: where GPUs traditionally have their own memory, separate from the main (CPU) memory, HSA requires these devices to share page tables so that devices can exchange data by sharing pointers. This is to be supported by custom memory management units.{{rp|6–7}} To render interoperability possible and also to ease various aspects of programming, HSA is intended to be ISA-agnostic for both CPUs and accelerators, and to support high-level programming languages.

So far, the HSA specifications cover:

===HSA Intermediate Layer===

HSAIL (Heterogeneous System Architecture Intermediate Language), a virtual instruction set for parallel programs

• similar{{according to whom|date=May 2015}} to LLVM Intermediate Representation and SPIR (used by OpenCL and Vulkan)
• finalized to a specific instruction set by a JIT compiler
• make late decisions on which core(s) should run a task
• explicitly parallel
• supports exceptions, virtual functions and other high-level features
• debugging support

• compatible with C++11, OpenCL, Java and .NET memory models
• relaxed consistency
• designed to support both managed languages (e.g. Java) and unmanaged languages (e.g. C)
• will make it much easier to develop 3rd-party compilers for a wide range of heterogeneous products programmed in Fortran, C++, C++ AMP, Java, et al.

• designed to enable heterogeneous task queueing: a work queue per core, distribution of work into queues, load balancing by work stealing
• any core can schedule work for any other, including itself
• significant reduction of overhead of scheduling work for a core

Mobile devices are one of the HSA's application areas, in which it yields improved power efficiency.

The illustrations below compare CPU-GPU coordination under HSA versus under traditional architectures.

{{Gallery

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| File:Desktop computer bus bandwidths.svg

| Standard architecture with a discrete GPU attached to the PCI Express bus. Zero-copy between the GPU and CPU is not possible due to distinct physical memories.

|File:HSA-enabled virtual memory with distinct graphics card.svg

| HSA brings unified virtual memory and facilitates passing pointers over PCI Express instead of copying the entire data.

| File:Integrated graphics with distinct memory allocation.svg

| In partitioned main memory, one part of the system memory is exclusively allocated to the GPU. As a result, zero-copy operation is not possible.

| File:HSA-enabled integrated graphics.svg

| Unified main memory, where GPU and CPU are HSA-enabled. This makes zero-copy operation possible.{{cite web |url=http://www.semiaccurate.com/2014/01/15/technical-look-amds-kaveri-architecture/ |title=Kaveri microarchitecture |date=2014-01-15 |work=SemiAccurate}}

| File:MMU and IOMMU.svg

| The CPU's MMU and the GPU's IOMMU must both comply with HSA hardware specifications.

}}

{{Clear}}

[[File:Linux AMD graphics stack.svg|thumb|upright=1.8|AMD GPUs contain certain additional functional units intended to be used as part of HSA. In Linux, kernel driver {{Mono|amdkfd}} provides required support.{{cite web

| url = https://www.phoronix.com/scan.php?page=news_item&px=MTc0NTk

| title = AMDKFD Driver Still Evolving For Open-Source HSA On Linux

| date = 21 July 2014 | access-date = 21 January 2015

| author = Michael Larabel | publisher = Phoronix

}}]]

Some of the HSA-specific features implemented in the hardware need to be supported by the operating system kernel and specific device drivers. For example, support for AMD Radeon and AMD FirePro graphics cards, and APUs based on Graphics Core Next (GCN), was merged into version 3.19 of the Linux kernel mainline, released on 8 February 2015.{{cite web

| url = http://kernelnewbies.org/Linux_3.19#head-ae54e026ef7588f4431f7e94178d27d5cd830bbf

| title = Linux kernel 3.19, Section 1.3. HSA driver for AMD GPU devices

| date = 8 February 2015 | access-date = 12 February 2015

| website = kernelnewbies.org

}} Programs do not interact directly with {{Mono|amdkfd}}{{Explain|date=December 2023}}, but queue their jobs utilizing the HSA runtime.{{cite web

| url = https://github.com/HSAFoundation/HSA-Runtime-Reference-Source/blob/master/README.md

| title = HSA-Runtime-Reference-Source/README.md at master

| date = 14 November 2014 | access-date = 12 February 2015

| website = github.com

}} This very first implementation, known as {{Mono|amdkfd}}, focuses on "Kaveri" or "Berlin" APUs and works alongside the existing Radeon kernel graphics driver.

Additionally, {{Mono|amdkfd}} supports heterogeneous queuing (HQ), which aims to simplify the distribution of computational jobs among multiple CPUs and GPUs from the programmer's perspective. Support for heterogeneous memory management (HMM), suited only for graphics hardware featuring version 2 of the AMD's IOMMU, was accepted into the Linux kernel mainline version 4.14.{{cite web|url=https://www.xda-developers.com/linux-kernel-414/|archive-url=https://web.archive.org/web/20171113231202/https://www.xda-developers.com/linux-kernel-414/|url-status=dead|archive-date=13 November 2017|title=Linux Kernel 4.14 Announced with Secure Memory Encryption and More|date=13 November 2017}}

Integrated support for HSA platforms has been announced for the "Sumatra" release of OpenJDK, due in 2015.{{cite web |url=http://www.hpcwire.com/2013/08/26/hsa_foundation_aims_to_boost_javas_gpu_prowess/ |title=HSA Foundation Aims to Boost Java's GPU Prowess |author=Alex Woodie |date=26 August 2013 |website=HPCwire}}

AMD APP SDK is AMD's proprietary software development kit targeting parallel computing, available for Microsoft Windows and Linux. Bolt is a C++ template library optimized for heterogeneous computing.{{cite web |url=https://github.com/HSA-Libraries/Bolt |title=Bolt on github|website=GitHub|date=11 January 2022}}

GPUOpen comprehends a couple of other software tools related to HSA. CodeXL version 2.0 includes an HSA profiler.{{cite web |url=http://gpuopen.com/codexl-2-0-is-here-and-open-source/ |title=CodeXL 2.0 includes HSA profiler |author=AMD GPUOpen |date=2016-04-19 |access-date=21 April 2016 |archive-date=27 June 2018 |archive-url=https://web.archive.org/web/20180627034628/https://gpuopen.com/codexl-2-0-is-here-and-open-source/ |url-status=dead }}

{{Clear}}

{{As of|2015|2}}, only AMD's "Kaveri" A-series APUs (cf. "Kaveri" desktop processors and "Kaveri" mobile processors) and Sony's PlayStation 4 allowed the integrated GPU to access memory via version 2 of the AMD's IOMMU. Earlier APUs (Trinity and Richland) included the version 2 IOMMU functionality, but only for use by an external GPU connected via PCI Express.{{Citation needed|date=June 2016}}

Post-2015 Carrizo and Bristol Ridge APUs also include the version 2 IOMMU functionality for the integrated GPU.{{Citation needed|date=June 2016}}

{{AMD APU features}}

ARM's Bifrost microarchitecture, as implemented in the Mali-G71,{{cite web |url=http://www.anandtech.com/show/10375/arm-unveils-bifrost-and-mali-g71/5 |title=ARM Bifrost GPU Architecture |date=2016-05-30}} is fully compliant with the HSA 1.1 hardware specifications. {{As of|2016|6}}, ARM has not announced software support that would use this hardware feature.

• General-purpose computing on graphics processing units (GPGPU)
• Non-Uniform Memory Access (NUMA)
• OpenMP
• Shared memory
• Zero-copy

{{Reflist|30em}}

{{Commons category}}

• {{YouTube|id=ln8JpfaLvbM|title=HSA Heterogeneous System Architecture Overview}} by Vinod Tipparaju at SC13 in November 2013
• [https://web.archive.org/web/20160514070602/http://www.mpsoc-forum.org/previous/2013/slides/8-Hegde.pdf HSA and the software ecosystem]
• [http://www-conf.slac.stanford.edu/xldb2012/talks/xldb2012_wed_1400_MichaelHouston.pdf 2012 – HSA by Michael Houston] {{Webarchive|url=https://web.archive.org/web/20160305141652/http://www-conf.slac.stanford.edu/xldb2012/talks/xldb2012_wed_1400_MichaelHouston.pdf |date=5 March 2016 }}

{{Use dmy dates|date=July 2019}}

Category:Heterogeneous computing