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Understanding Your HPC Application Needs

Many HPC applications began as single processor (single core) programs. If these applications take too long on a single core or need more memory than is available, they need to be modified so they can run on scalable systems. Fortunately, many of the important (and most used) HPC applications are already available for scalable systems. Not all applications require large numbers of cores for effective performance, while others are highly scalable. Here is how to better understand your HPC application needs.

Who Is Using HPC (and Why)?

In today’s highly competitive world, High Performance Computing (HPC) is a game changer. Though not as splashy as many other computing trends, the HPC market has continued to show steady growth and success over the last several decades. Market forecaster IDC expects the overall HPC market to hit $31 billion by 2019 while riding an 8.3% CAGR. The HPC market cuts across many sectors including academic, government, and industry. Learn which industries are using HPC and why.

InsideHPC Guide to Technical Computing

Today’s High Performance Computing (HPC) systems offer the ability to model everything from proteins to galaxies. The insights and discoveries offered by these systems are nothing short of astounding. Indeed, the ability to process, move, and store data at unprecedented levels, often reducing jobs from weeks to hours, continues to move science and technology forward at an accelerating pace. This article series offers those considering HPC, both users and managers, guidance when considering the best way to deploy an HPC solution.

Cloud Computing Models for HPC

This article describes the challenges that users face and the solutions available to make running cloud based HPC applications a reality. You’ll learn about different cloud computing models, potential economic savings and factors to consider when comparing an on-site data center with a cloud-based provider.

Accelerated Science: GPU Cluster Case Study

A successful example of how a well-managed GPU cluster allowed scientist to focus on obtaining results comes from the Tokyo University of Agriculture and Technology (TUAT) results. A research group lead by Dr. Akinori Yamanaka develops computation models and simulates engineering materials, for a variety of applications, using HPC. Using Bright Cluster Manager, Dr. Yamanaka and his team were able to immediately focus on algorithm development and not burden the team with cluster administration issues.

Developing a Plan for Cloud Based GPU Processing

For some applications, cloud based clusters may be limited due to communication and/or storage latency and speeds. With GPUs, however, these issue are not present because application running on cloud GPUs perform exactly the same as those in your local cluster — unless the application span multiple nodes and are sensitive to MPI speeds. For those GPU applications that can work well in the cloud environment, a remote cloud may be an attractive option for both production and feasibility studies.

Planning for the Convergence of HPC and Big Data

As an open source tool designed to navigate large amounts of data, Hadoop continues to find new uses in HPC. Managing a Hadoop cluster is different than managing an HPC cluster, however. It requires mastering some new concepts, but the hardware is basically the same and many Hadoop clusters now include GPUs to facilitate deep learning.

Six Strategies for Maximizing GPU Clusters

In a perfect world, there would be one version of all compilers, libraries, and profilers. To make things even easier, hardware would never change. However, technology marches forward, and such a world does not exist. Software tool features are updated, bugs are fixed, and performance is increased. Developers need these improvements but at the same time must manage these differences.

Best Practices for Maximizing GPU Resources in HPC Clusters

HPC developers want to write code and create new applications. The advanced nature of HPC often requires that this process be associated with specific hardware and software environment present on a given HPC resource. Developers want to extract the maximum performance from HPC hardware and at the same time not get mired down in the complexities of software tool chains and dependencies.

Strategies for Managing High Performance GPU Clusters

When discussing GPU accelerators, the focus is often on the price-to-performance benefits to the end user. The true cost of managing and using GPUs goes far beyond the hardware price, however. Understanding and managing these costs helps provide more efficient and productive systems.