Adaptive Computing : Market and Competitor Analysis

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Adaptive Computing is a software company specializing in workload and resource orchestration solutions for high-performance computing (HPC) environments. Their flagship product, Moab HPC Suite, optimizes job scheduling, resource utilization, and overall efficiency for large-scale computing systems. They cater to industries heavily reliant on HPC, including research, higher education, and various commercial sectors.

Company : Adaptive Computing

Industry : High-Performance ComputingWorkload ManagementResource Orchestration

Major Markets

United States

China

Japan

Key Competitors

Univa Corporation

Altair PBS Works

Slurm Workload Manager

Adaptive Computing Key Value propositions

Performance Optimization

Resource Utilization

Cost Efficiency

Workload Orchestration

Adaptive Computing Latest news

In the News Archives - Adaptive Computing In the News

NAPLES, FL, UNITED STATES, January 9, 2024 Adaptive Computing Enterprises, Inc. has announced the launch of its adaptive.ai-as-a-service offering to deliver ...

Adaptive Computing | LinkedIn

Feb 9, 2014 ... ... major cloud provider, and spin up data ... Check out Adaptive Computing's new HPC-AI offering! ... https://adaptivecomputing.com · 2 · Like Comment ...

Home - Adaptive Computing Adaptive Computing Home

Adaptive continues to meet demand in the areas of Machine Learning, Artificial Intelligence, Big Data, and HPC Cloud. ... News and Events. Adaptive Computing ...

qsub

If the destination is a routing queue, the job may be routed by the server to a new destination. If the -q option is not specified, the qsub command will submit ...

Adaptive Computing SWOT Analysis

Strengths

Strong product portfolio focused on HPC needs.Established customer base in research and government sectors.Expertise in workload management and optimization.

Weaknesses

Limited brand visibility compared to larger software vendors.Potential challenges in penetrating new industry verticals.Dependence on a specialized market segment.

Opportunities

Growing adoption of cloud computing in HPC.Increasing demand for AI and data-intensive workloads.Expansion into new geographical markets.

Threats

Competition from larger cloud providers offering HPC solutions.Rapid technological advancements requiring continuous innovation.Economic downturns impacting research and HPC budgets.

Top Marketing Strategies for Adaptive Computing

Personalized User Onboarding

Tailor the onboarding experience to the specific needs of each user, guiding them through the platform effectively and highlighting features relevant to their HPC workload management requirements. This will boost user adoption and retention by providing a more personalized and efficient onboarding experience.

In-depth Buyer's Guide Creation

Develop a comprehensive buyer's guide that addresses the specific challenges and needs of technical decision-makers in the HPC space, providing valuable insights on how Adaptive Computing's solutions can address their needs. This will position Adaptive Computing as a trusted resource and establish thought leadership within the HPC market.

Content-Specific Call-to-Action Optimization

Refine the call-to-actions within your content to be more relevant to the specific topics and user segments you are targeting. This will increase conversion rates by aligning the call-to-action with the user's immediate needs and interests, making it more likely for them to take the desired action.

Apply these strategies to your business with just one-click at Waxwing.ai

Adaptive Computing User Persona

Name:

David Wilson

Age :

45

Gender :

Male

Occupation :

CIO

Industry :

Higher Education

Education :

Master's Degree, Computer Science

Preferred platforms

GOALS

Optimize research computing resources to accelerate scientific discoveries and attract top researchers to the university
Implement a robust and scalable HPC infrastructure that can handle the growing demand for computational resources from various departments and research groups
Reduce the university's IT operational costs by improving resource utilization and minimizing downtime

Frustrations

Dealing with long queue times for researchers waiting to run their jobs on the HPC cluster
Facing difficulty in forecasting and scaling the HPC infrastructure to meet the fluctuating demands of research projects
Struggling to effectively manage software licenses and ensure researchers have access to the necessary tools

Name:

Sarah Chen

Age :

38

Gender :

Female

Occupation :

Lead Data Scientist

Industry :

Financial Services

Education :

Ph.D., Computational Physics

Preferred platforms

GOALS

Develop and deploy sophisticated financial models that require massive computational power to gain a competitive edge in the market
Leverage HPC resources to analyze large datasets and identify patterns and trends for algorithmic trading and risk management
Enhance the speed and efficiency of data-intensive tasks, such as backtesting trading strategies and running Monte Carlo simulations

Frustrations

Experiencing delays in model training and analysis due to limited availability of HPC resources
Finding it challenging to collaborate effectively with other data scientists and researchers on complex projects that involve large datasets and computations
Dealing with security concerns and ensuring the privacy of sensitive financial data when using shared computing resources

Name:

Mark Williams

Age :

42

Gender :

Male

Occupation :

IT Director

Industry :

Government Research

Education :

Bachelor's Degree, Information Technology

Preferred platforms

GOALS

Provide government researchers with secure and reliable access to high-performance computing resources for critical national projects
Ensure compliance with stringent government regulations and security protocols for data storage and processing
Maximize the return on investment for government funding by optimizing the utilization of expensive HPC infrastructure

Frustrations

Facing bureaucratic hurdles and lengthy approval processes for procuring and upgrading HPC hardware and software
Struggling to attract and retain skilled IT professionals with expertise in managing complex HPC environments
Dealing with the complexities of integrating HPC systems with existing legacy IT infrastructure and security protocols

Adaptive Computing Geographic and Demographic Insights

Geographic Insights: The United States and China are the largest markets for Adaptive Computing. Other important markets include Japan, Germany, and the United Kingdom.

Top Countries

United States
45.5%
China
20.2%
Japan
10.3%
Germany
8.8%
United Kingdom
7.7%

Demographic Insights: Adaptive Computing's target audience is predominantly male (70%) and falls within the age range of 35-54, aligning with experienced professionals in technical roles.

Age Distribution

Gender Distribution

Adaptive Computing Socio-economic Profile

Household and Income Insights: The majority of target users fall within the medium-income bracket and typically belong to households of 3-4 members.

Educational and Employment Insights: The target audience is primarily employed full-time, holding advanced degrees like University or College and Postgraduate qualifications.

Households Size

Income Distribution

Education Level

Employment Status

Adaptive Computing Behavioral Insights

Interest-Based Insights: Target users are interested in technology, cloud computing, big data, artificial intelligence, cybersecurity, data science, and scientific computing.

Technology and Social Media Usage: Users primarily leverage desktop devices (70%) and favor LinkedIn for professional networking, indicating a tech-savvy audience.

Interests

TechnologyCloud ComputingBig DataArtificial IntelligenceCybersecurityData ScienceScientific Computing

Device Breakdown

Social Media Usage

Adaptive Computing Top Competitors

Competitor	Estimated market share	Top domains
Univa Corporation	35.2%	Workload Management, Resource Optimization, Cloud Bursting
Altair PBS Works	28.7%	HPC Scheduling, Job Management, Cluster Optimization
Slurm Workload Manager	21%	Open-Source Workload Management, Job Scheduling, Resource Allocation

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