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Scientific Computing

Driving research forward with advanced computing.

Scientific Computing Redefined

At Solid Logix, we combine the strength of high-performance computing (HPC) with the agility of cloud-native and serverless technologies. Our solutions are designed to tackle the challenges of modern scientific research—enabling faster simulations, complex data analysis, and efficient workflows further optimized with scalable data models.

Cloud-Native Meets High Performance

In an era where breakthroughs hinge on the speed and accuracy of data-driven insights, Solid Logix brings next-generation scientific computing capabilities to the forefront. We specialize in high-performance computing integrated seamlessly with cloud-native and serverless frameworks. The result: your research and analytics run faster, scale effortlessly, and deliver tangible results that push scientific frontiers.

Big Data


Advancing Science with High-Performance Computing

HPC accelerates scientific breakthroughs by enabling massive parallel processing, rapid simulations, and data-driven insights.

Revolutionizing Research with HPC Solutions

Solid Logix leverages cutting-edge scientific computing to solve complex challenges in research and development. Our expertise enables organizations to scale their efforts and achieve impactful breakthroughs in data-driven fields.

Applications of Scientific Computing

Scientific computing empowers groundbreaking research and real-world applications by processing and analyzing massive datasets efficiently.

Seismic Simulation

Leverage HPC to model seismic events, aiding in infrastructure design, risk assessment, and disaster preparedness.

Geospatial Analysis

Process and analyze spatial data for applications in urban planning, resource management, and disaster response using GIS and HPC solutions.

Economic Forecasting

Utilize HPC to model economic systems and forecast market trends, empowering policymakers and financial institutions.

Genomic Research

Analyze genomic sequences at scale, enabling advancements in personalized medicine, drug development, and disease prevention.

Climate Modeling

Run simulations to predict weather patterns, assess climate risks, and study environmental changes.

Scientific Computing Visualization
Drug Discovery

Model molecular interactions and analyze massive chemical libraries to accelerate drug candidate identification.

Physics and Astronomy

Process astronomical data and simulate physical phenomena to uncover new insights about the universe.

Financial Modeling

Perform complex financial simulations and risk assessments to inform investment strategies and economic policies.

Robotics and Automation

Develop and test algorithms for autonomous systems, enhancing capabilities in robotics and industrial automation.

Geospatial Analysis

Analyze spatial data to support urban planning, environmental monitoring, and disaster response.

Benefits of Our Approach

Automatically resizing EMR clusters allows your organization to handle fluctuating workloads seamlessly. Whether you’re processing small datasets or running intensive simulations, our solutions dynamically allocate resources to match the demand. This eliminates manual intervention, ensures optimal performance, and avoids over-provisioning, saving both time and costs. With AWS Auto Scaling, you can maintain high availability while efficiently scaling clusters up or down.

Scientific computing often involves processing massive datasets or running compute-intensive tasks. By leveraging distributed frameworks like Apache Spark and GPU-accelerated instances, our solutions significantly reduce processing times. GPUs, particularly in fields like genomics or molecular modeling, deliver faster simulations and analytics, enabling researchers to achieve insights in hours instead of days. This speed empowers quicker decision-making and accelerates time-to-market for innovations.

Automating infrastructure provisioning and adopting event-driven computing simplifies complex operations. AWS CloudFormation enables Infrastructure-as-Code (IaC), allowing consistent and repeatable deployment of resources. Serverless technologies like AWS Lambda trigger processes automatically based on events, such as new data uploads or task completions. This approach not only reduces operational overhead but also ensures workflows are agile, efficient, and responsive to changes.

Our solutions minimize costs by incorporating serverless computing and spot instances. Serverless architectures, such as AWS Lambda, charge only for the actual compute time used, eliminating expenses tied to idle infrastructure. Additionally, by using AWS Spot Instances, you can access unused EC2 capacity at up to 90% lower prices, making it an ideal option for non-time-sensitive or flexible workloads. These strategies ensure maximum efficiency without compromising performance.

Ensuring the security and integrity of sensitive data is critical, especially in scientific computing. AWS provides robust compliance standards, including data encryption at rest and in transit, access control mechanisms, and detailed audit trails. Our solutions integrate these features to meet regulatory requirements such as HIPAA, GDPR, or FISMA. With fine-grained permissions and real-time monitoring, your data remains protected throughout its lifecycle, fostering trust and reliability.

Use Cases and Technologies

Process terabytes of genetic data efficiently using EMR clusters and AWS S3. Researchers can analyze DNA sequences, identify genetic markers, and accelerate breakthroughs in personalized medicine and disease prevention.
Key Technologies: Apache Spark on EMR, GPU instances, AWS Glue.

Run complex simulations to predict weather patterns, model climate change, and assess environmental risks. AWS enables dynamic scaling to handle seasonal or event-driven workload spikes.
Key Technologies: EMR with Hadoop, AWS Lambda, S3 and Glacier.

Simulate molecular interactions to identify potential drug candidates. Reduce the time and cost of drug discovery by leveraging high-performance computing for computational chemistry.
Key Technologies: GPU instances, machine learning models, S3.

Process massive datasets collected from telescopes to uncover insights about the universe. Researchers can analyze star patterns, cosmic radiation, and black hole data with AWS technologies.
Key Technologies: EMR with Apache Spark, S3 and Redshift, AWS Batch.

Monitor and predict equipment failures in research labs using machine learning models trained on historical data. This ensures minimal downtime and maximized productivity in experiments.
Key Technologies: EMR for IoT data, SageMaker, CloudWatch.

Analyze health trends and epidemiological data at scale to predict disease outbreaks and assess public health interventions. AWS enables fast, collaborative research using secure cloud infrastructure.
Key Technologies: EMR, Athena, Lambda.

Customers

Broad Institute Logo
Broad Institute

We partnered with the Broad Institute to implement cutting-edge scientific computing workflows, enabling researchers to process genomic data at unprecedented speeds and drive advancements in precision medicine.

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US Census Bureau

Our solution empowered Census Bureau statisticians to process and analyze vast amounts of Census data efficiently, improving accessibility and enabling faster decision-making.

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Moderna

We collaborated with Moderna to accelerate drug discovery using scientific computing solutions, optimizing processes to identify potential treatments faster and more effectively.

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World Health Organization (WHO)

For the WHO, we developed robust virus surveillance programs that leverage scientific computing to monitor and track virus patterns globally, aiding in early detection and response efforts.

Cloud-Native Meets High Performance

Scale your research effortlessly with elastic computing environments. We design systems that leverage both traditional HPC and cloud infrastructure to meet the demands of modern science.

Let’s Collaborate

Scientific Computing: Harnessing the Power of AWS

We specialize in scientific computing solutions that combine high-performance computing (HPC) with the flexibility and scalability of AWS. By leveraging cutting-edge technologies like EMR clusters and serverless computing, we enable researchers and organizations to process massive datasets, run complex simulations, and achieve breakthroughs faster and more cost-effectively.

AWS-Powered Scientific Workflows

1. Distributed Data Processing with EMR Clusters

Our solutions use AWS Elastic MapReduce (EMR) to process large-scale datasets with frameworks like Apache Spark and Hadoop. EMR clusters allow us to distribute computations across multiple nodes, enabling faster and more efficient data analysis.

2. Seamless Data Storage and Access with S3

We store and manage your data on AWS S3, a secure and scalable storage solution. Combined with EMR, this setup allows for quick access to large datasets while maintaining robust data integrity.

3. Event-Driven Processing with Lambda

For lightweight and event-triggered tasks, we use AWS Lambda to complement EMR workflows. This serverless approach ensures you only pay for the compute time you use, making it ideal for irregular workloads.

4. Accelerated Computations with GPU Instances

We integrate AWS GPU instances like P4 and G4 for high-performance computing tasks, such as machine learning, genomic analysis, and molecular simulations. This dramatically reduces processing times for compute-intensive applications.

5. Automation with CloudFormation and IaC

Our use of AWS CloudFormation enables automated provisioning of EMR clusters, storage, and supporting infrastructure. This Infrastructure-as-Code (IaC) approach ensures consistent, reliable setups tailored to your project needs.

6. Scalable Data Integration with Glue

AWS Glue helps us build seamless ETL pipelines, ensuring your data is clean, transformed, and ready for advanced analysis in EMR clusters or other AWS services.

CMR HPC