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![Heterogeneous Parallel Processor
• Trends to leveraging heterogeneous processors
– Cell BE[6], DSP, GPU, APU
– 50% of top 10 supercomputers use co-processors[2]
– 30% of top 10 supercomputers use NVIDIA GPU[2]
• What are the benefits?
– Cost-effectiveness
– Energy-efficiency
[2]Toop500.org. (2014). November 2014 | TOP500 Supercomputer Sites. Retrieved December 19, 2014, from
http://www.top500.org/lists/2014/11/](https://image.slidesharecdn.com/week1-hsa-fundamental-1-141226045956-conversion-gate01/85/HSA-Features-4-320.jpg)
![Heterogeneous Parallel Programming
Models
• Existing programming models
– Brook[3], Sh[4], CTM[5]
• Ancient era
• Utilize GLSL or HLSL to perform computation on GPU graphic
pipelines
– OpenCL 2.0, CUDA 6, DirectCompute 5
• Mainstream programming model
– OpenACC, C++AMP
• New high level programming models that aim to lower the
learning curve
• Performance optimization is a big challenge
• Stacked on mainstream programming models
– And more](https://image.slidesharecdn.com/week1-hsa-fundamental-1-141226045956-conversion-gate01/85/HSA-Features-5-320.jpg)
![Gap1 – long Runtime Latency
• The Mainstream programming model such as
OpenCL and CUDA
– Has extreme large runtime setup latency
• Device query, context initialization, program compilation,
user-space queue setup
• Runtime setup time grows with the number of GPU devices
– The kernel dispatch latency is high
• Require the help from traditional driver
• Not an optimal choice for consumer-level
applications with small workloads
– E.g. Adobe photoshop, spreadsheet functions[7]](https://image.slidesharecdn.com/week1-hsa-fundamental-1-141226045956-conversion-gate01/85/HSA-Features-6-320.jpg)
![Task Dispatch Model for Linux and
Windows
• Linux
• Windows[1]
• DRI/UMD prevents affects of single process crash to
the whole system
– large overhead is the penality
• Idea of directly talking to hardware and let the
hardware do the security and scheduling work
OpenCL DRI
DRI
Queue
DRM
Radeon
[1]AMD. (2013). HETEROGENEOUS SYSTEM ARCHITECTURE ( HSA ) AND THE SOFTWARE ECOSYSTEM.](https://image.slidesharecdn.com/week1-hsa-fundamental-1-141226045956-conversion-gate01/85/HSA-Features-7-320.jpg)
![HSA
• Heterogeneous System Architecture[8]
• Not-for-profit industry standard for programming
heterogeneous computing devices
• Founders:
– AMD
– Mobile solutions provider: ARM, MediaTek,
Qualcomm, TI
• Developments status:
– First version is in provisional phase](https://image.slidesharecdn.com/week1-hsa-fundamental-1-141226045956-conversion-gate01/85/HSA-Features-14-320.jpg)
![HSA Objectives
• A brand new heterogeneous programming
experiences
– Leads more performance
• Small form factors[1]
– Low power & energy efficiency
• HSA is not targeting on replacing mainstream
HPP model but provide a solid abstraction
layer of underlying heterogeneous resources](https://image.slidesharecdn.com/week1-hsa-fundamental-1-141226045956-conversion-gate01/85/HSA-Features-15-320.jpg)
![HSA Limitations
• We observed these limitations from the aspect of
implementing HSA on non-AMD platform
• Hardware support[6]
– Exception handling
• Floating point
• Page fault
– Hardware-based memory coherency
• hUMA
– Hardware-based signaling and synchronization
• hQ](https://image.slidesharecdn.com/week1-hsa-fundamental-1-141226045956-conversion-gate01/85/HSA-Features-22-320.jpg)
![References
• [1] AMD. (2013). HETEROGENEOUS SYSTEM ARCHITECTURE ( HSA ) AND THE
SOFTWARE ECOSYSTEM.
• [2] Toop500.org. (2014). November 2014 | TOP500 Supercomputer Sites. Retrieved
December 19, 2014, from http://www.top500.org/lists/2014/11/
• [3] Standford. (2003). BrookGPU. Retrieved December 19, 2014, from
http://graphics.stanford.edu/projects/brookgpu/
• [4] Intel. (2003). Sh: A high-level metaprogramming language for modern GPUs.
Retrieved December 19, 2014, from http://libsh.org/
• [5] AMD. (2007). Clost to the Metal. SIGGRAPH. Retrieved December 19, 2014,
from http://gpgpu.org/static/s2007/slides/07-CTM-overview.pdf
• [6] IBM. (2005). Cell Broadband Engine - IBM Microelectronics. Retrieved from
https://www-
01.ibm.com/chips/techlib/techlib.nsf/products/Cell_Broadband_Engine
• [7] LibreOffice. (2014). Calc: GPU enabling a spreadsheet. FOSDEM. Retrieved
December 17, 2014, from
https://archive.fosdem.org/2014/schedule/event/calc_gpu_enabling_a_spreadshe
et/
• [8] Foundation, HSA (2014). HSA Foundation. Retrieved December 19, 2014, from
http://www.hsafoundation.com/](https://image.slidesharecdn.com/week1-hsa-fundamental-1-141226045956-conversion-gate01/85/HSA-Features-24-320.jpg)
Uploaded byHen-Jung Wu
HSA Features
The document discusses the Heterogeneous System Architecture (HSA), focusing on its features, programming model, and the challenges of heterogeneous parallel programming. It outlines the gaps in current programming models, the benefits of HSA, and its objectives to improve performance and efficiency in computing. The text highlights the importance of a uniform memory space, user-level hardware queues, and the need for a solid abstraction layer for heterogeneous resources.
More Related Content
HSA Features
- 1. HSA Fundamentals (1) HenryWu 2014-12-19
- 2. Agenda • Background – CurrentGaps • Introductions to HSA – HSA Features • References
- 3. Agenda • Background – CurrentGaps • Introductions to HSA – HSA Features • HSA Programming Model • References
- 4. Heterogeneous Parallel Processor •Trends to leveraging heterogeneous processors – Cell BE[6], DSP, GPU, APU – 50% of top 10 supercomputers use co-processors[2] – 30% of top 10 supercomputers use NVIDIA GPU[2] • What are the benefits? – Cost-effectiveness – Energy-efficiency [2]Toop500.org. (2014). November 2014 | TOP500 Supercomputer Sites. Retrieved December 19, 2014, from http://www.top500.org/lists/2014/11/
- 5. Heterogeneous Parallel Programming Models •Existing programming models – Brook[3], Sh[4], CTM[5] • Ancient era • Utilize GLSL or HLSL to perform computation on GPU graphic pipelines – OpenCL 2.0, CUDA 6, DirectCompute 5 • Mainstream programming model – OpenACC, C++AMP • New high level programming models that aim to lower the learning curve • Performance optimization is a big challenge • Stacked on mainstream programming models – And more
- 6. Gap1 – longRuntime Latency • The Mainstream programming model such as OpenCL and CUDA – Has extreme large runtime setup latency • Device query, context initialization, program compilation, user-space queue setup • Runtime setup time grows with the number of GPU devices – The kernel dispatch latency is high • Require the help from traditional driver • Not an optimal choice for consumer-level applications with small workloads – E.g. Adobe photoshop, spreadsheet functions[7]
- 7. Task Dispatch Modelfor Linux and Windows • Linux • Windows[1] • DRI/UMD prevents affects of single process crash to the whole system – large overhead is the penality • Idea of directly talking to hardware and let the hardware do the security and scheduling work OpenCL DRI DRI Queue DRM Radeon [1]AMD. (2013). HETEROGENEOUS SYSTEM ARCHITECTURE ( HSA ) AND THE SOFTWARE ECOSYSTEM.
- 8. Gap2 – DistributedMemory Spaces • Heterogeneous programming model could be used to program – CPU + discrete GPU or DSP – CPU + fused GPU + discrete GPU – CPU + fused GPU + discrete GPUs + DSP anything … • With each compute unit has its own memory space – Several copies of same data exist at the same time – Data consistency is a problem • Memory consistency of various memory spaces in OpenCL is defined by GPU vendor – Increased complexity of data scheduling • Locality – Different layout of data may have impact to throughput • The idea is provide a uniformed memory view to all devices – Require a standard for all hardware devices to follow
- 9. Traditional Heterogeneous System •Low-speed PCIe data block transfer – Large overhead especially when transferring small block – Might overwhelm the benefit from utilizing GPU • Flatten the data layout before computation CPU Host Memory … … dGPU Graphic Memory Discrete GPU PCIe Bus CPU1 CPU1 CPU2 CPU2 CPUn
- 10. Heterogeneous System withAPU • NUMA based • Zero-copy transfer is possible – page must be pinned on memory • Management of various data duplication made programming error prone • Still lack support of complicated data structure, e.g. B tree – pointers Conventional APU Host Memory Dedicated Memory CPU1 CPU2 CPUn Fused GPU CPU1 CPU2 … … dGPU Graphic Memory Discrete GPU PCIe
- 11. Gap3 – DistributedMemory Spaces and Variety of Architectures • Heterogeneous programming model could be used to program – CPU + discrete GPU or DSP – CPU + fused GPU + discrete GPU – CPU + fused GPU + discrete GPUs + DSP anything … • With each compute unit may has its own ISA – Multiple version of kernel binaries needs to compiled on the run – Optimization is time consuming! • The idea: split compiling – provide an intermediate language • Low level enough to provide ahead of time optimization – And Just-in-Time compiler translate to final machine assembly • No compiler optimization to provide fast translation
- 12. Background Summary • HPPis the emerging trend • There exists a lot of HPP models, but learning curve is high • Complexity of HPP is increasing – Data orchestra – Task scheduling – Multiple versions of kernel codes to run on various of heterogeneous processors
- 13. Agenda – Thisweek • Background • Introductions to HSA – HSA Features • HSA Programming Model • References
- 14. HSA • Heterogeneous SystemArchitecture[8] • Not-for-profit industry standard for programming heterogeneous computing devices • Founders: – AMD – Mobile solutions provider: ARM, MediaTek, Qualcomm, TI • Developments status: – First version is in provisional phase
- 15. HSA Objectives • Abrand new heterogeneous programming experiences – Leads more performance • Small form factors[1] – Low power & energy efficiency • HSA is not targeting on replacing mainstream HPP model but provide a solid abstraction layer of underlying heterogeneous resources
- 16. High Level HSAfeatures • Computation-focused programming model for heterogeneous architectures – No need to worry about data movement – Thin runtime • hUMA, Uniform memory space for all participated CU – Bi-directional coherence – Pageable memory – Pointer support • hQ, User-mode hardware queue – No more dependence to traditional graphic driver – Negligible dispatch latency • GPU context switch – QoS scheduling policies • Assembly-level intermediate language – eliminate processor heterogeneity – HSAIL
- 17. HSA-hUMA • Compute Unitsinclude CPUs and GPUs share a uniform memory view • Memory consistency and coherence are provided • Pointer is used, no more data block transfer
- 18. HSA - hUMA •hUMA Heterogeneous System Architecture Uniform Memory Space CPU1 CPU2 CPUn hCU CPU1 CPU2 … … hCU hCU hCU hCU hCU hCU hCU … …
- 19. HSA -hQ • hQ –User-level hardware queue – Hardware receive tasks directly from application
- 20.
- 21. AMD HSA ROADMAP STEPPEEAGLE KAVERI Merlin Falcon CORRIZO
- 22. HSA Limitations • Weobserved these limitations from the aspect of implementing HSA on non-AMD platform • Hardware support[6] – Exception handling • Floating point • Page fault – Hardware-based memory coherency • hUMA – Hardware-based signaling and synchronization • hQ
- 23. Agenda • Background • Introductionsto HSA – HSA Features • References
- 24. References • [1] AMD.(2013). HETEROGENEOUS SYSTEM ARCHITECTURE ( HSA ) AND THE SOFTWARE ECOSYSTEM. • [2] Toop500.org. (2014). November 2014 | TOP500 Supercomputer Sites. Retrieved December 19, 2014, from http://www.top500.org/lists/2014/11/ • [3] Standford. (2003). BrookGPU. Retrieved December 19, 2014, from http://graphics.stanford.edu/projects/brookgpu/ • [4] Intel. (2003). Sh: A high-level metaprogramming language for modern GPUs. Retrieved December 19, 2014, from http://libsh.org/ • [5] AMD. (2007). Clost to the Metal. SIGGRAPH. Retrieved December 19, 2014, from http://gpgpu.org/static/s2007/slides/07-CTM-overview.pdf • [6] IBM. (2005). Cell Broadband Engine - IBM Microelectronics. Retrieved from https://www- 01.ibm.com/chips/techlib/techlib.nsf/products/Cell_Broadband_Engine • [7] LibreOffice. (2014). Calc: GPU enabling a spreadsheet. FOSDEM. Retrieved December 17, 2014, from https://archive.fosdem.org/2014/schedule/event/calc_gpu_enabling_a_spreadshe et/ • [8] Foundation, HSA (2014). HSA Foundation. Retrieved December 19, 2014, from http://www.hsafoundation.com/
Editor's Notes
- #3 This week we introduce briefly to HSA programming model, we will explore the technical details of hQ and hUMA next week.
- #4 This week we introduce briefly to HSA programming model, we will explore the technical details of hQ and hUMA next week.
- #14 This week we introduce briefly to HSA programming model, we will explore the technical details of hQ and hUMA next week.
- #16 Small form factor: GPU on the same die Capability of use fused GPU computation power Mobile device especially
- #24 This week we introduce briefly to HSA programming model, we will explore the technical details of hQ and hUMA next week.