GPU_FPGA_Briefing+March+2017

GPU, FPGA and HPC instance update on March 2017 by Jamie Kinney at JAWS AI x HPC 2017/03/31

Transcript

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Jamie Kinney, Principal Product Manager, HPC March, 2017 Accelerated Computing on AWS
2. EC2 Compute Instance Types M4 General purpose Compute optimized Storage and IO optimized GPU and FPGA accelerated Memory optimized X1 2010 2013 2016 2016 Preview F1 P2 G2 CG1 M3 T2 I2 HS1 I3 D2 R4 R3 C5 C4 C3 CC2 Announced
3. GPU Acceleration with P2 Instances
4. P2 GPU Instances • Up to 16 GPUs (8 NVIDIA K80 cards) in a single instance • Including peer-to-peer PCIe GPU interconnect • Supporting a wide variety of use cases including deep learning, HPC simulations, and batch rendering Instance Size GPUs GPU Peer to Peer vCPUs Memory (GiB) Network Bandwidth* p2.xlarge 1 - 4 61 1.25Gbps p2.8xlarge 8 Y 32 488 10Gbps p2.16xlarge 16 Y 64 732 20Gbps *In a placement group P2
5. Deep Learning on GPUs P2 GPU instances for high performance DL training and inference MXNet training on EC2 P2 instances: We trained a popular image analysis algorithm, Inception v3, using MXNet and running on P2 instances MXNet had the fastest throughput of any library we evaluated (as measured by the number of images trained per second), and the throughput rose by almost the same rate as the number of GPUs used for training, with a scaling efficiency of 85%.
6. Deep Learning Frameworks
7. Medical Image Rendering on P2
8. New: Elastic GPU
9. EC2 + Elastic GPU = Graphics Flexibility t2 c4 m4 r4 : : Small GPU : : Large GPU Attach Elastic GPU to an instance at launch, similar to attaching an EBS volume
10. Elastic GPU Architecture Compute + Graphics Instructions Graphics Instructions Instructions Image Image Instance Graphics Attachment
11. Attaching Elastic GPU – Console
12. Attaching Elastic GPU – Console
13. Fully managed application streaming service that provides users instant access to their desktop applications
14. Desktop Application Streaming Stream desktop applications securely to any web browser Pay-as-you-go Scale globally Secure apps & data Run Desktop Apps in a Web Browser
15. • Use multiple apps at the same time • Clipboard, file upload/download, printing • Audio and bandwidth controls • Multiple storage options • HTML5 browsers with no plug-ins Simple User Experience
16. Simple user experience
17. Simple user experience
18. FPGA Acceleration with F1 Instances
19. GPU and FPGA for Accelerated Computing NVIDIA Tesla GPU Card P2: GPU-accelerated computing § Enabling a high degree of parallelism – each GPU has thousands of cores § Consistent, well documented set of APIs (CUDA, OpenACC, OpenCL) § Supported by a wide variety of ISVs and open source frameworks Xilinx UltraScale+ FPGA F1: FPGA-accelerated computing § Massively parallel – each FPGA includes millions of parallel system logic cells § Flexible – no fixed instruction set, can implement wide or narrow datapaths § Programmable using available, cloud-based FPGA development tools
20. Accelerated Computing Concepts Parallelism increases throughout… CPU: High speed, low efficiency GPU/FPGA: High throughput, high efficiency GPUs and FPGAs can provide massive parallelism and higher efficiency than CPUs for certain categories of applications
21. A GPU is effective at processing the same set of operations in parallel – single instruction, multiple data (SIMD). A GPU has a well-defined instruction-set, and fixed word sizes – for example single, double, or half-precision integer and floating point values. An FPGA is effective at processing the same or different operations in parallel – multiple instructions, multiple data (MIMD). An FPGA does not have a predefined instruction-set, or a fixed data width. Control ALU ALU Cache DRAM ALU ALU CPU (one core) FPGA DRAM DRAM GPU Each FPGA in F1 has more than 2M of these cells Each GPU in P2 has 2880 of these cores DRAM Parallel Processing in GPUs and FPGAs Block RAM Block RAM DRAM DRAM
22. F1 FPGA Instances • Up to 8 Xilinx Virtex UltraScale Plus VU9p FPGAs in a single instance with four high-speed DDR-4 per FPGA • Largest size includes high performance FPGA interconnects via PCIe Gen3 (FPGA Direct), and bidirectional ring (FPGA Link) • Designed for hardware-accelerated applications including financial computing, genomics, accelerated search, and image processing Instance Size FPGAs FPGA Link FPGA Direct vCPUs Memory (GiB) NVMe Instance Storage Network Bandwidth* f1.2xlarge 1 - 8 122 1 x 480 5 Gbps f1.16xlarge 8 Y Y 64 976 4 x 960 20 Gbps *In a placement group F1
23. AWS FPGA Shell FPGA I/O is provided using pre-configured, pre-tested, and secure I/O components, allowing FPGA developers to focus on their differentiating value The FPGA Shell allows for faster coding of core acceleration functions by removing the need to develop I/O related FPGA hardware Block RAM Block RAM DDR-4 DDR-4 DDR-4 DDR-4 FPGA Link PCIe Abstracting FPGA I/O
24. Amazon Machine Image (AMI) Amazon FPGA Image (AFI) EC2 F1 Instance CPU Application on F1 DDR-4 Attached Memory DDR-4 Attached Memory DDR-4 Attached Memory DDR-4 Attached Memory DDR-4 Attached Memory DDR-4 Attached Memory DDR-4 Attached Memory DDR-4 Attached Memory FPGA Link PCIe DDR Controllers Launch Instance and Load AFI An F1 instance can have any number of AFIs An AFI can be loaded into the FPGA in less than 1 second FPGA Acceleration Using F1 F1 FPGA Direct
25. Development steps Launch the AWS-provided FPGA Developer AMI, which includes all needed FPGA design and programming software, as well as the AWS FPGA Hardware Development Kit (HDK) Use Xilinx Vivado or SDAccel software and a hardware description language (Verilog, VHDL, or OpenCL) with the HDK to describe and simulate your custom FPGA logic After successful simulation, use Vivado or SCAccel to synthesize and place/route the FPGA logic to create an FPGA Design Check Point (DCP), encrypt, and generate an Amazon FPGA Image (AFI) Launch an F1 instance and load the AFI to the FPGA, using AFI management tools provided by AWS Developing Applications for F1 1 2 3 4
26. Highly Efficient • Algorithms Implemented in Hardware • Gate-Level Circuit Design • No Instruction Set Overhead Massively Parallel • Massively Parallel Circuits • Multiple Compute Engines • Rapid FPGA Reconfigurability FPGA Speeds Analysis of Whole Human Genomes from Hours to Minutes Unprecedented Low Cost for Compute and Compressed Storage Genomics Processing
27. Modeling Counterparty Risk (CVA) and Regulatory Capital Requirements Financial Computing
28. F1 for Video Processing Next Generation Video Compression for Broadcast Quality 4K content Successfully ported to F1 in just 3 weeks
29. F1 for Accelerated Analytics Heterogeneous Compute Acceleration for Faster Data Discovery
30. Delivering FPGA Partner Solutions on AWS via AWS Marketplace Amazon EC2 FPGA Deployment Amazon Machine Image (AMI) Amazon FPGA Image (AFI) Customers AWS Marketplace
31. Thank You dpelleri@amazon.com