Serverless is great for web applications and APIs, but this does not mean it cannot be used successfully for other use cases. In this talk, we will discuss a successful application of serverless in the field of High Performance Computing. Specifically we will discuss how Lambda, Fargate, Kinesis and other serverless technologies are being used to run sophisticated financial models at one of the major reinsurance companies in the World. We we learn about the architecture, the tradeoffs, some challenges and some unresolved pain points. Most importantly, we'll find out if serverless can be a great fit for HPC and if we can finally stop managing those boring EC2 instances!

1. Serverless for HPC
Luciano Mammino
fourTheorem
@loige

2. Diamond Sponsor
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3. 👋 Hello, I am Luciano
Senior architect
nodejsdesignpatterns.com
Let’s connect:
🌎 loige.co
🐦 @loige
🎥 loige
🧳 lucianomammino

4. Middy Framework
SLIC Starter - Serverless Accelerator
SLIC Watch - Observability Plugin
Business focused technologists.
Accelerated Serverless | AI as a Service | Platform Modernisation

5. We host a podcast about AWS and Cloud computing
🔗 awsbites.com
🎬 YouTube Channel
🎙 Podcast
📅 Episodes every week
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6. Get the slides: fth.link/cd22
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7. Agenda
● The 6 Rs of Cloud Migration
● A serverless case study
○ The problem space and types of workflows
○ Original on premise implementation
○ The PoC
○ The final production version
○ The components of a serverless job scheduler
○ Challenges & Limits
fth.link/cd22
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8. The 6 Rs of Cloud Migrations
🗑 🕸 🚚
Retire Retain Rehost
🏗 📐 💰
Replatform Refactor Repurchase
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fth.link/cd22

9. A case study
Case study on AWS blog:
fth.link/awshpc
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10. The workloads - Risk Rollup
🏦 Financial modeling to understand the portfolio of risk
🧠 Internal, custom-built risk model on all reinsurance deals
⚙ HPC (High-Performance Computing) workload
🗄 ~45TB data processed
⏱ 2/3 rollups per day (6-8 hours each!)
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11. The workloads - Deal Analytics
⚡ Near real-time deal pricing using the same risk model
🗃 Lower data volumes
🔁 High frequency of execution – up to 1.000 per day
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12. Original on-prem implementation
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13. Challenges
🐢 Long execution times, constraining business agility
🥊 Competing workloads
📈 Limits our ability to support portfolio growth
😩 Can’t deliver new features
🧾 Very high total cost of ownership
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14. Thinking Big
💭 Imagine a solution that would …
1. Offer a dramatic increase in performance
2. Provide consistent run times
3. Support more executions, more often
4. Support future portfolio growth and new
capabilities – 15x data volumes
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15. The Goal ⚽
Run a Risk Rollup in 1 hour!
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16. Architecture Options for Compute/Orchestration
AWS Lambda
Amazon SQS AWS Step Functions
AWS Fargate
Com t om :
Red he b to si l ,
s a l , ev -d i n
co n s
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17. POC Architecture
AWS Batch
S3
Step Functions
Lambda
SQS
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18. Measure Everything! 📏
⏱ Built metrics in from the start
󰤈 AWS metrics we wish existed out of the box:
- Number of running containers
- Success/failure counts
🎨 Custom metrics:
- Scheduler overhead
- Detailed timings (job duration, I/O time, algorithm steps)
🛠 Using CloudWatch, EMF
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19. Measure Everything! 📏
👍 Rollup in 1 hour
☁ Running on AWS Batch
👎 Cluster utilisation was <50%
✅ Goal success
🤔 Understanding of what needs to
be addressed next!
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20. Beyond the PoC
Production: optimise for unique workload characteristics
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21. Job Plan
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22. In reality, not all jobs are alike!
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23. Horizontal scaling 🚀
1000’s of jobs
Duration: 1 second – 45 minutes
Scaling horizontally = splitting jobs
Jobs split according to their
complexity/duration
Resulting in >1 million jobs
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24. Moving to production 🚢
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25. Scope
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26. Actual End to End overview
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27. Modelling Worker
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28. Compute Services
Scales to 1000’s of tasks (containers)
Little management overhead
Up to 4 vCPUs and 30GB Memory
Up to 200GB ephemeral storage
Scales to 1000’s of function containers (in seconds!)
Very little management overhead
Up to 6 vCPUs and 10GB Memory
Up to 10GB ephemeral storage
It wasn’t always this way!
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29. Store all the things in S3!
The source of truth for:
● Input Data (JSON, Parquet)
● Intermediate Data (Parquet)
● Results (Parquet)
● Aggregates (Parquet)
Input data: 20GB
Output data: ~1 TB
Reads and writes: 10,000s of objects per second.
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30. Scheduling and Orchestration
✅ We have our cluster (Fargate or Lambda)
✅ We have a plan! (list of jobs, parameters and
dependencies)
🤔 How do we feed this plan to the cluster?!
🤨 Existing schedulers use traditional clusters – there
is no serverless job scheduler for workloads like this!
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31. Lifecycle of a Job
A new job
get queued
here 👇
A worker
picks up the
job and
executes it
The worker
emits the
job state
(success or
failure)
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32. Event-Driven Scheduler
Job states are pulled
from a Kinesis Data
Stream
Redis stores:
- Job states
- Dependencies
This scheduler checks
new job states against
the state in Redis and
figures out if there are
new jobs that can be
scheduled next
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33. Dynamic Runtime
Handling
We also need to handle
system failures!
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34. Outcomes 🙌
Business
● Rollup in 1 hour
● Removed limits on number of runs
● Faster, more consistent deal analytics
● Business spending more time on
revenue-generating activities
● Support portfolio growth and deliver new
capabilities
Technology
● Brought serverless to HPC financial
modeling
● Reduced codebase by ~70%
● Lowered total cost of ownership
● Increased dev team agility
● Reduced carbon footprint
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35. Hitting the limits 😰
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36. S3 Throughput
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37. S3 Partitioning
S3 cleverly detects high-throughput prefixes and creates partitions
….normally
If this does not happen…
🚨Please reduce your request rate;
Status Code: 503; Error Code: SlowDown
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38. The Solution
Explicit Partitioning:
○Figure out how many partitions you need
○Update code to create keys uniformly distributed over all partitions
/part/0…
/part/1…
/part/2…
/part/3…
…
/part/f…
1. Talk (a lot) to AWS SAs, Support, Account
Manager for special requirements like this!
2. Think ahead if you have multiple accounts
for different environments!
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39. Fargate Scaling
●We want to run 3000 containers ASAP
●This took > 1 hour!
●We built a custom Fargate scaler
○Using the RunTask API (no ECS Service)
○Hidden quota increases
○Step Function + Lambda
●3000 containers in ~20 minutes
The AWS ECS team since made lots of
improvements, making it possible to scale to
3,000 containers in under 5 minutes
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40. How high can we go today?
🚀 10,000 concurrent Lambda functions in seconds
🎢 10,000 Fargate containers in 10 minutes
💸 No additional cost
vladionescu.me/posts/scaling-containers-on-aws-in-2022
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41. Wrapping up 🎁
● "Serverless supercomputer" lets you do HPC with
commodity AWS compute
● Plenty of challenges, but it's doable!
● Agility and innovation benefits are massive
● Customer is now serverless-first and expert in AWS
Other interesting case studies:
☁ AWS HTC Grid - 🧬 COVID genome research
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42. Special thanks to @eoins and @cmthorne10
fth.link/cd22
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