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Introduction to High Performance Computing (HPC)

When we talk about high performance computing we are typically trying to solve some type of problem. These problems will generally fall into one of four types:

  • Compute Intensive – A single problem requiring a large amount of computation.
  • Memory Intensive – A single problem requiring a large amount of memory.
  • Data Intensive – A single problem operating on a large data set.
  • High Throughput – Many unrelated problems that are be computed in bulk.


In this post, I will provide a detailed introduction to High Performance Computing (HPC) that can help organizations solve the common issues listed above.

Compute Intensive Workloads

First, let us take a look at compute intensive problems. The goal is to distribute the work for a single problem across multiple CPUs to reduce the execution time as much as possible. In order for us to do this, we need to execute steps of the problem in parallel. Each process­—or thread—takes a portion of the work and performs the computations concurrently. The CPUs typically need to exchange information rapidly, requiring specialization communication hardware. Examples of these types of problems are those that can be found when analyzing data that is relative to tasks like financial modeling and risk exposure in both traditional business and healthcare use cases. This is probably the largest portion of HPC problem sets and is the traditional domain of HPC.

When attempting to solve compute intensive problems, we may think that adding more CPUs will reduce our execution time. This is not always true. Most parallel code bases have what we call a “scaling limit”. This is in no small part due to the system overhead of managing more copies, but also to more basic constraints.


This is summed up brilliantly in Amdahl’s law.

In computer architecture, Amdahl’s law is a formula which gives the theoretical speedup in latency of the execution of a task at fixed workload that can be expected of a system whose resources are improved. It is named after computer scientist Gene Amdahl, and was presented at the AFIPS Spring Joint Computer Conference in 1967.

Amdahl’s law is often used in parallel computing to predict the theoretical speedup when using multiple processors. For example, if a program needs 20 hours using a single processor core, and a particular part of the program which takes one hour to execute cannot be parallelized, while the remaining 19 hours (p = 0.95) of execution time can be parallelized, then regardless of how many processors are devoted to a parallelized execution of this program, the minimum execution time cannot be less than that critical one hour. Hence, the theoretical speedup is limited to at most 20 times (1/(1 − p) = 20). For this reason, parallel computing with many processors is useful only for very parallelizable programs.

– Wikipedia

Amdahl’s law can be formulated the following way:



  • Slatency is the theoretical speedup of the execution of the whole task;
  • s is the speedup of the part of the task that benefits from improved system resources;
  • p is the proportion of execution time that the part benefiting from improved resources originally occupied.



Chart Example: If 95% of the program can be parallelized, the theoretical maximum speedup using parallel computing would be 20 times.

Bottom line: As you create more sections of your problem that are able to run concurrently, you can split the work between more processors and thus, achieve more benefits. However, due to complexity and overhead, eventually using more CPUs becomes detrimental instead of actually helping.

There are libraries that help with parallelization, like OpenMP or Open MPI, but before moving to these libraries, we should strive to optimize performance on a single CPU, then make p as large as possible.

Memory Intensive Workloads

Memory intensive workloads require large pools of memory rather than multiple CPUs. In my opinion, these are some of the hardest problems to solve and typically require great care when building machines for your system. Coding and porting is easier because memory will appear seamless, allowing for a single system image.  Optimization becomes harder, however, as we get further away from the original creation date of your machines because of component uniformity. Traditionally, in the data center, you don’t replace every single server every three years. If we want more resources in our cluster, and we want performance to be uniform, non-uniform memory produces actual latency. We also have to think about the interconnect between the CPU and the memory.

Nowadays, many of these concerns have been eliminated by commodity servers. We can ask for thousands of the same instance type with the same specs and hardware, and companies like Amazon Web Services are happy to let us use them.

Data Intensive Workloads

This is probably the most common workload we find today, and probably the type with the most buzz. These are known as “Big Data” workloads. Data Intensive workloads are the type of workloads suitable for software packages like Hadoop or MapReduce. We distribute the data for a single problem across multiple CPUs to reduce the overall execution time. The same work may be done on each data segment, though not always the case. This is essentially the inverse of a memory intensive workload in that rapid movement of data to and from disk is more important than the interconnect. The type of problems being solved in these workloads tend to be Life Science (genomics) in the academic field and have a wide reach in commercial applications, particularly around user data and interactions.

High Throughput Workloads

Batch processing jobs (jobs with almost trivial operations to perform in parallel as well as jobs with little to no inter-CPU communication) are considered High Throughput workloads. In high throughput workloads, we create an emphasis on throughput over a period rather than performance on any single problem. We distribute multiple problems independently across multiple CPU’s to reduce overall execution time. These workloads should:

  • Break up naturally into independent pieces
  • Have little or no inter-cpu communcation
  • Be performed in separate processes or threads on a separate CPU (concurrently)


Workloads that are compute intensive jobs can likely be broken into high throughput jobs, however, high throughput jobs do not necessarily mean they are CPU intensive.

HPC On Amazon Web Services

Amazon Web Services (AWS) provides on-demand scalability and elasticity for a wide variety of computational and data-intensive workloads, including workloads that represent many of the world’s most challenging computing problems: engineering simulations, financial risk analyses, molecular dynamics, weather prediction, and many more.   

– AWS: An Introduction to High Performance Computing on AWS

Amazon literally has everything you could possibly want in an HPC platform. For every type of workload listed here, AWS has one or more instance classes to match and numerous sizes in each class, allowing you to get very granular in the provisioning of your clusters.

Speaking of provisioning, there is even a tool called CfnCluster which creates clusters for HPC use. CfnCluster is a tool used to build and manage High Performance Computing (HPC) clusters on AWS. Once created, you can log into your cluster via the master node where you will have access to standard HPC tools such as schedulers, shared storage, and an MPI environment.

For data intensive workloads, there a number of options to help get your data closer to your compute resources.

  • S3
  • Redshift
  • DynamoDB
  • RDS


EBS is even a viable option for creating large scale parallel file systems to meet high-volume, high-performance, and throughput requirements of workloads.

HPC Workloads & 2nd Watch

2nd Watch can help you solve complex science, engineering, and business problems using applications that require high bandwidth, enhanced networking, and very high compute capabilities.

Increase the speed of research by running high performance computing in the cloud and reduce costs by paying for only the resources that you use, without large capital investments. With 2nd Watch, you have access to a full-bisection, high bandwidth network for tightly-coupled, IO-intensive workloads, which enables you to scale out across thousands of cores for throughput-oriented applications. Contact us today to learn more.

2nd Watch Customer Success

Celgene is an American biotechnology company that manufactures drug therapies for cancer and inflammatory disorders. Read more about their cloud journey and how they went from doing research jobs that previously took weeks or months, to just hours. Read the case study.

We have also helped a global finance & insurance firm prove their liquidity time and time again in the aftermath of the 2008 recession. By leveraging the batch computing solution that we provided for them, they are now able to scale out their computations across 120,000 cores while validating their liquidity with no CAPEX investment. Read the case study.


– Lars Cromley, Director of Engineering, Automation, 2nd Watch


2nd Watch Meets Customer Demands and Prepares for Continued Growth and Acceleration with Amazon Aurora

The Product Development team at 2nd Watch is responsible for many technology environments that support our software and solutions—and ultimately, our customers. These environments need to be easily built, maintained, and kept in sync. In 2016, 2nd Watch performed an analysis on the amount of AWS billing data that we had collected and the number of payer accounts we had processed over the course of the previous year.  Our analysis showed that these measurements had more than tripled from 2015 and projections showed that we will continue to grow at the same, rapid pace with AWS usage and client onboarding increasing daily. Knowing that the storage of data is critical for many systems, our Product Development team underwent an evaluation of the database architecture used to house our company’s billing data—a single SQL Server instance running a Web edition of SQL Server with the maximum number of EBS volumes attached.

During the evaluation, areas such as performance, scaling, availability, maintenance and cost were considered and deemed most important for future success. The evaluation revealed that our current billing database architecture could not meet the criteria laid out to keep pace with growth.  Considerations were made to increase the storage capacity by one VM to the maximum family size or potentially upgrade to MS SQL Enterprise. In either scenario, the cost of the MS SQL instance doubled.  The only option for scaling without substantially increasing our cost was to scale vertically, however, to do so would result in diminishing performance gains. Maintenance of the database had become a full-time job that was increasingly difficult to manage.

Ultimately, we chose the cloud-native solution, Amazon Aurora, for its scalability, low-risk, easy-to-use technology.  Amazon Aurora is a MySQL relational database that provides speed and reliability while being delivered at a lower cost. It offers greater than 99.99% availability and can store up to 64TB of data. Aurora is self-healing and fully managed, which, along with the other key features, made Amazon Aurora an easy choice as we continue to meet the AWS billing usage demands of our customers and prepare for future growth.

The conversion from MS SQL to Amazon Aurora was successfully completed in early 2017 and, with the benefits and features that Amazon Aurora offers, many gains were made in multiple areas. Product Development can now reduce the complexity of database schemas because of the way Aurora stores data. For example, a database with one hundred tables and hundreds of stored procedures was reduced to one table with 10 stored procedures. Gains were made in performance as well. The billing system produces thousands of queries per minute and Amazon Aurora handles the load with the ability to scale to accommodate the increasing number of queries. Maintenance of the Amazon Aurora system is now virtually managed. Tasks such as database backups are automated without the complicated task of managing disks. Additionally, data is copied across six replicas in three availability zones which ensures availability and durability.

With Amazon Aurora, every environment is now easily built and setup using Terraform. All infrastructure is automatically setup—from the web tier to the database tier—with Amazon CloudWatch logs to alert the company when issues occur. Data can easily be imported using automated processes and even anonymized if there is sensitive data or the environment is used to demo to our customers. With the conversion of our database architecture from a single MS SQL Service instance to Amazon Aurora, our Product Development team can now focus on accelerating development instead of maintaining its data storage system.