Evaluation of FPGAs resurgence for hardware acceleration applied to computed tomography 3D Tomography back- projection parallelization on FPGAs using OpenCL Presented by : Maxime MARTELLI , 1 st year PhD Student L2S, SATIE, TSA 1 2017 GPU Winter School, Grenoble, FR
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3D Tomography back- projection parallelization on FPGAs using … · GPU vs FPGA with OpenCL An embedded GPU is more energy efficient Algorithm inadequacy implies longer FPGA execution
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Evaluation of FPGAs resurgence for hardware acceleration applied to computed tomography
3D Tomography back-projection parallelization on
FPGAs using OpenCL
Presented by : Maxime MARTELLI , 1st year PhD Student
L2S, SATIE, TSA
1
2017 GPU Winter School, Grenoble, FR
CONTEXT
Moore’s law end announced for 2021
Architecture Algorithm Adequacy- Granular hardware specialization - Processors will offload specific processing to a suited architecture
Software FPGA design tools multiplication
2
HYPOTHESISThe idea
Does HLS tools progress means a resurgence of FPGAs for computed tomography?
3
With the rise of Accelerator-as-a-Service (AaaS), what is the future landscape for FPGAs?
Summary
4
I. What is OpenCL ?II. Why use HLS on FPGAs ?III. Use case highlightIV.OpenCL Memory modelV. Custom implementationsVI.Conclusion and perspectives
I. WHAT IS OPENCL?
5
• Open, royalty-free standard for parallel, compute intensive applica
tion development
• Initiated by Apple, specification maintained by the Khronos group
• Supports multiple device classes, CPUs, GPUs, DSPs, Cell, etc.
• First release on December 2008
• Specification currently at version 2.0
• SDKs and tools are provided by compliant device vendors
OpenCL basics
6
• Proprietary technology for GPGPU programming from Nvidi
a
• Not just API and tools, but name for the whole architecture
• Targets Nvidia hardware and GPUs only
• First SDK released February 2007
• SDK and tools available to 32- and 64-bit Windows, Linux a
nd Mac OS
• Tools and SDK are available for free from Nvidia.
CUDA basics
7
Basics compared
8
CUDA OpenCLWhat it is HW architecture,
programming language, API, SDK
and tools
Open API and language
specification
Propietary or open technology
Proprietary Open and royalty-free
When introduced Q4 2006 Q4 2008SDK vendor Nvidia Implementation
vendorsFree SDK Yes Depends on vendor
Heterogeneous device support
No, just NVIDIA GPUs
Yes (Apple, Nvidia, AMD, IBM, Intel,
…)
OpenCL Memory Architecture
9
CUDA Memory Architecture
10
OpenCL Execution model
11
II. WHY USE HLS ON FPGAS ?
12
Field Programmable Gate Array (FPGA)
13Programmable Switch FabricSource : Intel
CPU instruction mapping
14Source : Intel
CPU execution path (1)
15Source : Intel
CPU execution path (2)
16Source : Intel
CPU vs FPGA execution
17Source : Intel
• Custom data-path that matches your algorithms
• Uses exactly what you need (Operation, Data Width, memory
configuration, …)
• Timing closure and reduced power consumption
• Much easier programming than VHDL
Advantages of FPGA HLS
18
II. USE CASE HIGHLIGHT
19
Brief history
In 2004, FPGA were widely used in Tomography
For 10 years now, GPU dominates the field
With the evolution of HLS tools, a new interest for FPGAs emerge