Google Cloud and Synopsys Inc are partnering to help semiconductor companies drive Electronic Design Automation (EDA) innovation in the cloud to accelerate time to market, and lower costs across the entire semiconductor product life cycle. Synopsys is the industry’s largest provider of EDA technology used in the design and verification of integrated circuits, or semiconductor chips. Bringing Synopsys technology onto Google Cloud enables customers to benefit from scalable cloud bursting and complementary licensing models to help them quickly deploy and scale Synopsys’ EDA tools. Semiconductor companies can significantly accelerate chip design and production, increase designer productivity, and empower innovation in power-performance-area optimization.

Google Cloud Platform has enabled high performance computing workloads, such as those used by today’s semiconductor chip designers, to deliver exceptionally fast results and reduce prototyping from years to months. Google Cloud’s HPC services have delivered highly scalable solutions across multiple industries – from scientific research to autonomous vehicle testing and simulation.

EDA software is a large consumer of high performance computing capacity in the cloud. With the release of Synopsys Cloud bring-your-own-cloud (BYOC) solution on Google Cloud, chip designers can now scale their Google Cloud infrastructure with Synopsys’s leading EDA tools under the flexible FlexEDA pay-per-use model and access unlimited EDA software license availability on-demand by the hour or minute.

The Synopsys Cloud BYOC deployment architecture on Google Cloud is enabled through their unique cloud metering service which uses Google Cloud regional MIGs (Managed Instance Groups) for autoscaling and multi-zone deployment. This enables the service to scale up to meet customer workload demands and scale down to optimize costs. Secrets used by Synopsys Cloud are securely stored in Google Secret Manager and usage data is encrypted using Google Cloud key management, providing customers with a highly secure design environment.

Synopsys Cloud also leverages Google Cloud’s Ops Agent and Operations Suite Dashboards are used to show metric data, alerting policies, and log entries, providing customers with detailed analytics visibility to make better chip design project lifecycle management decisions. The Synopsys Cloud BYOC solution has been validated with EDA workloads scaling out to thousands of cores on Google Cloud, using Google Filestore network file storage during validation to provide the highest throughput performance.

Vikram Bhatia, Head, Synopsys Cloud Product Management, Synopsys said, “With the release of Synopsys Cloud BYOC solution on Google Cloud, we are transforming the way our mutual semiconductor customers can design the chips of the future. Google Cloud has been leading the innovation wave for semiconductors in the cloud and we are excited about being an early adopter in leveraging those innovations for our unique EDA offerings on the cloud.” Customers can evaluate a full featured Synopsys Cloud BYOC environment on Google Cloud for free by signing up at: synopsys.com/cloud.

“Combined with GCP’s unique platform services for AI, security, and shared storage, the Synopsys Cloud BYOC solution creates a compelling package for semiconductor designers who will create the next generation of chips for the world’s insatiable needs” says Simon Floyd, Industry Director, Manufacturing & Transportation, Google Cloud.

Google Cloud provides everything you need, including free Google Cloud credits to get you up and running. Click here to learn more about Semiconductors on Google Cloud.

The ability to gain real-time insights from IoT data can redefine competitiveness for businesses. Intelligence allows connected devices and assets to interact efficiently with applications and with human beings in an intuitive and non-disruptive way. After your IoT project is up and running, many devices will be producing lots of data. You need an efficient, scalable, affordable way to both manage those devices and handle all that information. 

IoT Core is a fully managed service for managing IoT devices. It supports registration, authentication, and authorization inside the Google Cloud resource hierarchy as well as device metadata stored in the cloud, and the ability to send device configuration from other GCP or third-party services to devices. 

Main components

The main components of Cloud IoT Core are the device manager and the protocol bridges:

• The device manager  registers devices with the service, so you can then monitor and configure them. It provides:
  • Device identity management 
  • Support for configuring, updating, and controlling individual devices
  • Role-level access control
  • Console and APIs for device deployment and monitoring
• Two protocol bridges (MQTT and HTTP) can be used by devices to connect to Google Cloud Platform for:
  • Bi-directional messaging
  • Automatic load balancing
  • Global data access with Pub/Sub

How does Cloud IoT Core work?

Device telemetry data is forwarded to a Cloud Pub/Sub topic, which can then be used to trigger Cloud Functions as well as other third-party apps to consume the data. You can also perform streaming analysis with Dataflow or custom analysis with your own subscribers.

Cloud IoT Core supports direct device connections as well as gateway-based architectures. In both cases the real time state of the device and the operational data is ingested into Cloud IoT Core and the key and certificates at the edge are also managed by Cloud IoT Core. From Pub/Sub the raw input is fed into Dataflow for transformation, and the cleaned output is populated in Cloud Bigtable for real-time monitoring or BigQuery for warehousing and machine learning. From BigQuery the data can be used for visualization in Looker or Data Studio and it can be used in Vertex AI for creating machine learning models. The models created can be deployed at the edge using Edge Manager (in experimental phase). Device configuration updates or device commands can be triggered by Cloud Functions or Dataflow to Cloud IoT Core, which then updates the device.  

Design principles of Cloud IoT Core

As a managed service to securely connect, manage, and ingest data from global device fleets, Cloud IoT COre is designed to be:

• Flexible, providing easy provisioning of device identities and enabling devices to access most of Google Cloud
• IThe industry leader in IoT scalability and performance
•  Interoperable, with supports for the most common industry-standard IoT protocols

Use cases

IoT use cases range across numerous industries. Some typical examples include:

• Asset tracking, visual inspection, and quality control in retail, automotive, industrial, supply chain and logistics
• Remote monitoring and predictive maintenance in oil & gas, utilities, manufacturing, and transportation
• Connected homes and consumer technologies.
• Vision intelligence in retail, security, manufacturing, and industrial sectors
• Smart living in commercial, residential, and smart spaces 
• Smart factories with predictive maintenance and real-time plant floor analytics

 For a more in-depth look into Cloud IoT Core check out the documentation.  

https://youtube.com/watch?v=76v16P-Wqe4%3Fenablejsapi%3D1%26

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

Records are made to be broken. In 2019, we calculated 31.4 trillion digits of π — a world record at the time. Then, in 2021, scientists at the University of Applied Sciences of the Grisons calculated another 31.4 trillion digits of the constant, bringing the total up to 62.8 trillion decimal places. Today we’re announcing yet another record: 100 trillion digits of π.

This is the second time we’ve used Google Cloud to calculate a record number1 of digits for the mathematical constant, tripling the number of digits in just three years.

This achievement is a testament to how much faster Google Cloud infrastructure gets, year in, year out. The underlying technology that made this possible is Compute Engine, Google Cloud’s secure and customizable compute service, and its several recent additions and improvements: the Compute Engine N2 machine family, 100 Gbps egress bandwidth, Google Virtual NIC, and balanced Persistent Disks. It’s a long list, but we’ll explain each feature one by one.

Before we dive into the tech, here’s an overview of the job we ran to calculate our 100 trillion digits of π.

• Program: y-cruncher v0.7.8, by Alexander J. Yee
• Algorithm: Chudnovsky algorithm
• Compute node: n2-highmem-128 with 128 vCPUs and 864 GB RAM
• Start time: Thu Oct 14 04:45:44 2021 UTC
• End time: Mon Mar 21 04:16:52 2022 UTC
• Total elapsed time: 157 days, 23 hours, 31 minutes and 7.651 seconds
• Total storage size: 663 TB available, 515 TB used
• Total I/O: 43.5 PB read, 38.5 PB written, 82 PB total

Architecture overview

Calculating π is compute-, storage-, and network-intensive. Here’s how we configured our Compute Engine environment for the challenge.

For storage, we estimated the size of the temporary storage required for the calculation to be around 554 TB. The maximum persistent disk capacity that you can attach to a single virtual machine is 257 TB, which is often enough for traditional single node applications, but not in this case. We designed a cluster of one computational node and 32 storage nodes, for a total of 64 iSCSI block storage targets.

The main compute node is a n2-highmem-128 machine running Debian Linux 11, with 128 vCPUs and 864 GB of memory, and 100 Gbps egress bandwidth support. The higher bandwidth support is a critical requirement for the system as we adopted a network-based shared storage architecture.

Each storage server is a n2-highcpu-16 machine configured with two 10,359 GB zonal balanced persistent disks. The N2 machine series provides balanced price/performance, and when configured with 16 vCPUs it provides a network bandwidth of 32 Gbps, with an option to use the latest Intel Ice Lake CPU platform, which makes it a good choice for high-performance storage servers.

Automating the solution

We used Terraform to set up and manage the cluster. We also wrote a couple of shell scripts to automate critical tasks such as deleting old snapshots, and restarting from snapshots (we didn’t need to use this though). The Terraform scripts created OS guest policies to help ensure that the required software packages were automatically installed. Part of the guest OS setup process was handled by startup scripts. In this way, we were able to recreate the entire cluster with just a few commands.

We knew the calculation would run for several months and even a small performance difference could change the runtime by days or possibly weeks. There are also a number of combinations of parameters in the operating system, infrastructure, and application itself. Terraform helped us test dozens of different infrastructure options in a short time. We also developed a small program that runs y-cruncher with different parameters and automated a significant portion of the measurement. Overall, the final design for this calculation was about twice as fast as our first design. In other words, the calculation could’ve taken 300 days instead of 157 days!

The scripts we used are available on GitHub if you want to look at the actual code that we used to calculate the 100 trillion digits.

Choosing the right machine type for the job

Compute Engine offers machine types that support compute- and I/O-intensive workloads. The amount of available memory and network bandwidth were the two most important factors, so we selected n2-highmem-128 (Intel Xeon, 128 vCPUs and 864 GB RAM). It satisfied our requirements: high-performance CPU, large memory, and 100 Gbps egress bandwidth. This VM shape is part of the most popular general purpose VM family in Google Cloud.

100 Gbps networking

The n2-highmem-128 machine type’s support for up to 100 Gbps of egress throughput was also critical. Back in 2019 when we did our 31.4-trillion digit calculation, egress throughput was only 16 Gbps, meaning that bandwidth has increased by 600% in just three years. This increase was a big factor that made this 100-trillion experiment possible, allowing us to move 82.0 PB of data for the calculation, up from 19.1 PB in 2019.

We also changed the network driver from virtio to the new Google Virtual NIC (gVNIC). gVNIC is a new device driver and tightly integrates with Google’s Andromeda virtual network stack to help achieve higher throughput and lower latency. It is also a requirement for 100 Gbps egress bandwidth.

Storage design

Our choice of storage was crucial to the success of this cluster – in terms of capacity, performance, reliability, cost and more. Because the dataset doesn’t fit into main memory, the speed of the storage system was the bottleneck of the calculation. We needed a robust, durable storage system that could handle petabytes of data without any loss or corruption, while fully utilizing the 100 Gbps bandwidth.

Persistent Disk (PD) is a durable high-performance storage option for Compute Engine virtual machines. For this job we decided to use balanced PD, a new type of persistent disk that offers up to 1,200 MB/s read and write throughput and 15-80k IOPS, for about 60% of the cost of SSD PDs. This storage profile is a sweet spot for y-cruncher, which needs high throughput and medium IOPS.

Using Terraform, we tested different combinations of storage node counts, iSCSI targets per node, machine types, and disk size. From those tests, we determined that 32 nodes and 64 disks would likely achieve the best performance for this particular workload.

We scheduled backups automatically every two days using a shell script that checks the time since the last snapshots, runs the fstrim command to discard all unused blocks, and runs the gcloud compute disks snapshot command to create PD snapshots. The gcloud command returns and y-cruncher resumes calculations after a few seconds while the Compute Engine infrastructure copies the data blocks asynchronously in the background, minimizing downtime for the backups.

To store the final results, we attached two 50 TB disks directly to the compute node. Those disks weren’t used until the very last moment, so we didn’t allocate the full capacity until y-cruncher reached the final steps of the calculation, saving four months worth of storage costs for 100 TB.

Results

All this fine tuning and benchmarking got us to the one-hundred trillionth digit of π — 0. We verified the final numbers with another algorithm (Bailey–Borwein–Plouffe formula) when the calculation was completed. This verification was the scariest moment of the entire process because there is no sure way of knowing whether or not the calculation was successful until it finished, five months after it began. Happily, the Bailey-Borwein-Plouffe formula found that our results were valid. Woo-hoo! Here are the last 100 digits of the result:

4658718895 1242883556 4671544483 9873493812 1206904813
2656719174 5255431487 2142102057 7077336434 3095295560

You can also access the entire sequence of numbers on our demo site.

So what?

You may not need to calculate trillions of decimals of π, but this massive calculation demonstrates how Google Cloud’s flexible infrastructure lets teams around the world push the boundaries of scientific experimentation. It’s also an example of the reliability of our products – the program ran for more than five months without node failures, and handled every bit in the 82 PB of disk I/O correctly. The improvements to our infrastructure and products over the last three years made this calculation possible.

Running this calculation was great fun, and we hope that this blog post has given you some ideas about how to use Google Cloud’s scalable compute, networking, and storage infrastructure for your own high performance computing workloads. To get started, we’ve created a codelab where you can create and calculate pi on a Compute Engine virtual machine with step-by-step instructions. And for more on the history of calculating pi, check out this post on The Keyword. Here’s to breaking the next record!

1. We are actively working with Guinness World Records to secure their official validation of this feat as a “World Record”, but we couldn’t wait to share it with the world. This record has been reviewed and validated by Alexander J. Yee, the author of y-cruncher.

The partnership between Google Cloud and SAP is entering a new chapter. Over the years, our close partnership with SAP and our mutual dedication to customers has inspired us to do some of our most unique and innovative work—such as building Fast Restart and Memory Poisoning Recovery capabilities for S/4HANA workloads, offering real-time access to our advanced machine learning (ML) capabilities, and developing integrations with the industry’s best cloud security and network infrastructure.

With an expanded, strategic partnership, we are capitalizing on one of the cloud’s most valuable benefits: enabling customer choice without driving up cost or complexity. Customers have shared that they appreciate what Google Cloud has done over the years to support their ability to choose—from our support for multi-cloud environments to our track record with open-source technologies like Kubernetes, to name a couple of examples. 

“We chose Google Cloud to support our SAP implementation. Our decision had a lot to do with the relationship between Google Cloud and SAP and also for the applications and services that are offered by Google Cloud, like BigQuery, which are helping to enable data and analytics within our organization.”— Sam Moses, Vice President of Corporate Systems, The Home Depot

Now, we’re placing this same emphasis on customer choice, flexibility, and freedom to help SAP customers get greater ROI from their cloud investments in three ways:

• Execute seamless business transformations that deliver all of the benefits of modern, cloud-native applications and services—quickly, economically, and without disrupting day-to-day operations
• Migrate critical business systems to the cloud, in order to lock in OpEx savings and refocus their IT teams on strategic business technology initiatives
• Augment existing business systems with cloud capabilities in AI, ML, and analytics

The cornerstone of our expanded partnership is the RISE with SAP program—a comprehensive set of tools, technology, and expert support for a fast, predictable, cost-effective path to business transformation with SAP in the cloud. RISE is also designed to meet every SAP customer on their own terms, whether they’re undertaking an enterprise-wide cloud migration or pursuing an incremental or hybrid-cloud approach. 

To support customers taking advantage of RISE with SAP, Google Cloud will work even more closely with SAP to accelerate customers’ cloud migrations and business process modernization across several key areas. This includes ensuring global availability of multiple SAP services and products—such as SAP Business Technology Platform and SAP HANA Cloud—on Google Cloud’s reliable, scalable infrastructure and high-speed network. Google Cloud is also committed to bringing SAP’s solution for RPA in manufacturing to the Google Cloud Marketplace to support business process modernization. 

Additionally, integrating our AI, ML, and analytics capabilities with key SAP solutions will help companies augment their business systems with sophisticated capabilities and technologies so they can draw more value out of the data stored with them.

“We can deliver our 160 key business insights two or three orders of magnitude faster with BigQuery. When you think about our marketing campaigns and month-end finance processes, we’re talking about jobs that used to take five to eight hours to complete that are now running in seconds.”— Dan Semmens, Head of Data and AI, ATB Financial

Join us on the journey ahead

There’s so much more to come from SAP and Google Cloud. Learn more about our commitment to enabling digital transformation and hear more from customers about their SAP on Google Cloud deployments.