In-Network Computing. Paving the Road to Exascale. June 2017

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1 In-Network Computing Paving the Road to Exascale June 2017

2 Exponential Data Growth The Need for Intelligent and Faster Interconnect -Centric (Onload) Data-Centric (Offload) Must Wait for the Data Creates Performance Bottlenecks Analyze Data as it Moves! Faster Data Speeds and In-Network Computing Enable Higher Performance and Scale 2017 Mellanox Technologies 2

3 Data Centric Architecture to Overcome Latency Bottlenecks -Centric (Onload) Data-Centric (Offload) Network In-Network Computing HPC / Machine Learning Communications Latencies of 30-40us HPC / Machine Learning Communications Latencies of 3-4us Intelligent Interconnect Paves the Road to Exascale Performance 2017 Mellanox Technologies 3

4 In-Network Computing to Enable Data-Centric Data Center In-Network Computing Key for Highest Return on Investment 2017 Mellanox Technologies 4

5 In-Network Computing to Enable Data-Centric Data Centers RDMA CORE-Direct Tag-Matching SHARP SHIELD Direct NVMe over Fabrics Security Security Programmable (FPGA) NVMe Storage Programmable (ARM) In-Network Computing Key for Highest Return on Investment 2017 Mellanox Technologies 5

6 Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) Reliable Scalable General Purpose Primitive In-network Tree based aggregation mechanism Large number of groups Multiple simultaneous outstanding operations Applicable to Multiple Use-cases HPC Applications using MPI / SHMEM Distributed Machine Learning applications SHARP Tree Root Scalable High Performance Collective Offload Barrier, Reduce, All-Reduce, Broadcast and more Sum, Min, Max, Min-loc, max-loc, OR, XOR, AND Integer and Floating-Point, 16/32/64/128 bits SHARP Tree Aggregation Node (Process running on HCA) SHARP Tree Endnode (Process running on HCA) SHArP Tree 2017 Mellanox Technologies 6

7 Latency (usec) Latency (usec) Allreduce Performance Allreduce Latency (8 Bytes, 128 Bytes) Allreduce Latency (1K Bytes, 2K Bytes) Number of Nodes (1PPN) Number of Nodes (1PPN) 2017 Mellanox Technologies 7

8 OpenFOAM Application Performance OpenFOAM is a popular computational fluid dynamics application Software In-Network Computing 2017 Mellanox Technologies 8

9 Tag Matching Logistics 2017 Mellanox Technologies 9

10 Tag Matching Common Implementation Protocols 2017 Mellanox Technologies 10

11 Tag Matching Hardware Implementation Overview 2017 Mellanox Technologies 11

12 Lower is better Lower is better MPI Tag-Matching Offload Advantages 31% 97% 31% lower latency and 97% lower utilization for MPI operations Performance comparisons based on ConnectX Mellanox Technologies 12

13 In-Network Computing to Enable Data-Centric Data Center In-Network Computing In-Network Memory Self-Healing In-Network Computing Key for Highest Return on Investment 2017 Mellanox Technologies 13

14 The SHIELD Self Healing Interconnect Technology Software-based solutions for network failures create long delays: 5-30 seconds for 1K to 10K node clusters During software-based recovery time, data can be lost, applications can fail Adaptive Routing creates further issues (failing links may act as black holes ) Mellanox SHIELD technology is an innovative hardware-based solution SHIELD technology enables the generation of Self-Healing Interconnect The ability to overcome network failures by the network intelligent devices Accelerates network recovery time by 5000X Highly scalable and available for EDR and HDR solutions and beyond Self-Healing Network Enables Unbreakable Data Centers 2017 Mellanox Technologies 14

15 Consider a Flow From A to B Data Server A Server B 2017 Mellanox Technologies 15

16 The Simple Case: Local Fix Data Server A Server B 2017 Mellanox Technologies 16

17 The Remote Case: Using FRN s (Fault Recovery Notifications) FRN Data Data Server A Server B 2017 Mellanox Technologies 17

18 In-Network Computing Enables Deep Learning Frameworks Middleware (MPI, grpc) - Optional CUDA SHARP rcuda Direct RDMA NVMe over Fabrics Mellanox Interconnect Solutions Mellanox Accelerations for Machine Learning and Big Data 2017 Mellanox Technologies 18

19 2X Acceleration for Baidu Machine Learning Software from Baidu Usage: word prediction, translation, image processing RDMA (Direct) speeds training Lowers latency, increases throughput More cores for training Even better results with optimized RDMA ~2X Acceleration for Paddle Training with RDMA 2017 Mellanox Technologies 19

20 The Ever Growing Demand for Higher Performance Performance Development Terascale Petascale Exascale 1 st Roadrunner The Interconnect is the Enabling Technology HW APP SW Application Software Hardware SMP to Clusters Single-Core to Many-Core Co-Design 2017 Mellanox Technologies 20

21 Thank You

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