(1) AWS Parallel Cluster can accelerate high performance scientific computing by providing flexible compute clusters in AWS that are optimized for HPC workloads. (2) Case studies show how computational chemistry and deep learning for drug discovery benefit from HPC resources for tasks like molecular simulations and property predictions. (3) AWS offers compute instances, storage, networking and other services for building HPC clusters, along with tools like Parallel Cluster that simplify setup and management of these clusters for scientific applications.

1. How can AWS Parallel Cluster
accelerate high performance
scientific computing?
Rangsiman Ketkaew | November 24, 2019
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2. $ whoami
- Rangsiman (Nutt) Ketkaew – since 1993
- BSc and MSc in Physical Chemistry
- Research assistant in computational chemistry
- Remote system administrator at stealth startup (part time)
- Linux (RHEL/CentOS/Ubuntu) user since 2012
- 2-years-experiences user of AWS & HPC
- Linux, HPC and Scientific Computing lover
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Cutt.ly/rangsiman1993 Github.com/rangsimanketkaew

3. Outline
(1) Introduction to High Performance Scientific Computing
(2) Case studies
(1) Computational Chemistry
(2) Deep Learning-driven for Drug Discovery
(3) Introduction to AWS HPC
(4) Setting up AWS Parallel Cluster for high performance calculation
(5) Showcase
(6) Conclusion
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High Performance Computing (HPC)

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- The ability to process data and perform
complex calculations at high speeds.
- Supercomputer with a high level of
performance as compared to a general-
purpose computer.
- The performance of a supercomputer is
commonly measured in floating-
point operations per second (FLOPS).
What is HPC?

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SUMMIT #1 Fastest Supercomputer

7. Computing on the cluster
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Scientific Computing

9. Popular HPC Workloads
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Genomics
Processing
Modeling and
Simulation
Government and
Educational Research
Monte Carlo
Simulations
Transcoding and
Encoding
Computational
Chemistry

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Case Studies of Scientific Computing

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Case Studies of Scientific Computing
- Computational Chemistry

12. What is Computational Chemistry?
- Relies on results of theoretical chemistry and computer science
- Practice of efficient computer calculations to obtain chemical and physical
properties of molecules, liquids and solids using theoretical method.
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+ =

13. What do computational chemist do?
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14. Popular computational methods
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- Quantum chemistry methods
- HF, MPn, CASSCF, CI, CC,EOM-CC, …
- DFT, PW-DFT, …
- Molecular dynamics
- Newtonian equation of motion
- Mesoscale simulation
- Monte Carlo simulation
- Chemical dynamics

15. Some Applications
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Synthesis
Zeolite
Catalyst
Chemical reaction
Polymer
Protein

16. Life isn’t a bed of roses.
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17. Life isn’t a bed of roses.
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Case Studies of Scientific Computing
- Deep Learning-driven for Drug Discovery

19. Drug Discovery
One drug
10 - 15 years
$1 billion
100 – 1000 people
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20. Drug Discovery
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Protein-Ligand binding (Lock and Key model)

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❑ Property prediction
❑ Given a compound, predict biochemical properties
❑ Given a compound and a protein, predict their binding affinity
❑ Generative chemistry
❑ Given a set of goals, generate a series of new molecule (maybe from
a starting one) that best optimize those goals
What Deep Learning can do for Drug Design

22. Drug Discovery
+ Deep Learning
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General workflow of applying deep learning for drug discovery

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Drug Discovery
+ GANs

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Scientific Computing with AWS

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26. What we needs
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High-end hardware
Low cost
Flexible and Scalable
Cross-node MPI connection
Observability of resources
Shared disk storage
Share and Collaborate
Amazon Elastic Cloud Cluster (EC2)
Amazon Compute Fleet
Amazon ParallelCluster
Amazon AutoScaling
Amazon CloudFormation
Amazon CloudWatch
Amazon Elastic Block Store (EBS)
Amazon Elastic File System (EFS)
Amazon FSx for Lustre
Amazon Simple Storage Service (S3)
Amazon API Dashboard

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AWS is Here for Us

28. EC2: Instance types
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- General Purpose
- Compute Optimized
- Memory Optimized + Enhanced Networking + Fast Storage
- Accelerated Computing
- Storage Optimized
- Instance Features
- Measuring Instance Performance

29. EC2: Compute Optimized
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Instance: C5, C5n, C4
- 2nd Generation (Cascade)
- 1st Generation (Skylake)
- Up to 96 vCPU cores
- Memory 192 GiB with 4 x 900 NVMe SSD
- Network bandwidth: 25 Gbps
- EBS bandwidth: 14000 Mbps

30. Shared memory
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- Communication of multiple people
cleaning a house.
- Open Memory Programming (OpenMP) is
commonly used in the HPC community.
Distributed memory
- The way how multiple humans interact while
solving problems.
- Every process (person) 'works' on it's own and
can communicate with the others by sending
messages (talking and listening).
- Message Passing Interface (MPI) is used for
communicating between the cores.

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• Intel® Advanced Vector Instructions (Intel® AVX-512)
Intel® AVX-512 can handle your most demanding computational tasks and
accelerate performance for workloads o Intel® AVX 512 delivers up to 2X more
FLOPs/clock-cycle for HPC, analytics, cryptography and data compression workloads5.
• Intel Software development Tools
Allow you to take advantage of the Intel Xeon Scalable processor and quickly
deploy a fully elastic HPC cluster on AWS Cloud. Once created, the cluster provisions
standard HPC tools such as schedulers, Intel Parallel Studio XE, Mathematics Kernel
Library (MKL), Message Passing Interface (MPI) environment, and shared storage.

32. EC2: Accelerated Computing
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Instance: P3, P2, G4, G3, F1
- 2nd Generation (Cascade)
- NVIDIA Tesla T4 Tensor
- 320 Turing Tensor Cores
- Single Precision Performance: 8.1 TFLOPS
- Mixed Precision Performance: 65 TFLOPS
- Memory Capacity: 16 GB GDDR6
- Banswidth: 320+ GB/s

33. Operating Systems
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Amazon Machine Images (AMIs) are preconfigured with an
ever-growing list of operating systems.

34. Other necessary AWS tools
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- AWS Command Line Interface (CLI)
- $ pip install awscli

35. Other necessary AWS tools
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- AWS ParallelCluster (CfnCluster)
- $ pip install aws-parallelcluster

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Building AWS Parallel Cluster

37. Parallel Cluster
Configuration
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Parallel Cluster
Configuration

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Parallel Cluster
Configuration

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Parallel Cluster
Configuration

41. Parallel Cluster
Configuration
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Parallel Cluster
Configuration

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Parallel Cluster
Configuration

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Parallel Cluster
Configuration

45. Example of AWS dashboard
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46. Example of General architecture of the cluster
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Benchmarking the performance of cluster with HPL
https://cutt.ly/hpc-linpack

48. Job Scheduler with Parallel Code
Shared memory Distributed memory Hybrid memory
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49. Cost Optimization
On-Demand Reserved Spot
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For spiky workloads,
or to define needs
For committed
utilization
For high-scale, time-
flexible workloads

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Sharing is so important

51. CompEnv-AWS-EC2
- Workflow for building
AWS EC2
- Setup Parallel Cluster for
scientific computing
- User-friendly
documentation
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github.com/rangsimanketkaew/compenv-aws-ec2

52. AWS HPC’s Functionality and Features
• Create or select a pre-configured AMI
• Pause and resume your instances
• Optimized CPU and GPU instances
• High performance computing
• Flexible storage option
• Enhance networking
• Auto Scaling of the cluster on-the-fly
• Elastic (static) IP address
• Pay as you go (Let’s keep in mind terminating the compute node before leaving!)
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53. Final Remarks
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- AWS HPC helps scientific research, academic organizations, government
agencies, and commercial HPC users gain fast access to grid and cluster
computing resources.
- The users can achieve results faster and with higher quality, at a reduced cost
relative to traditional HPC infrastructure.
- The AWS HPC transforms previously complex and static HPC infrastructures into
highly flexible and adaptable resources for on-demand or long-term use.
- AWS HPC can accelerate the process of solving complicated scientific problems.

54. References
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- TOP500 List
- Intel
- NVIDIA
- AWS official website, documentations and articles
- Machine Learning and Drug Discovery by Amir Saffari, BeneloventAI
- Running network constrained applications on EC2 by Patrick Cullen
- Testing Amazon EC2 network speed by EPAM cloud
- Novartis Uses AWS to Conduct 39 Years of Computational Chemistry In
9 Hours, Steve Litster, Novartis

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All questions are welcome
rangsiman1993@gmail.com
fb.com/rangsiman1993
@rangsiman1993
Cutt.ly/rangsiman1993
rangsiman1993 Github.com/rangsimanketkaew
"If I have seen further, it is by standing upon the shoulders of giants“
Sir Isaac Newton

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