Optimize computing resources on-premises and in the cloud by visualizing and analyzing complex flows and identifying their inherent parallelism with HPCWorks Flowtracer.
Create, manage and execute design flows across a variety of applications, including semiconductor design and software development and easily handle the latest artificial intelligence (AI) and machine learning workflows. Manage dependencies between files and tools with our unique tracing technique.
Increase productivity by visualizing and controlling complex workloads, including demanding AI workloads, with high-performance flow definition and secure connectivity. Run flows locally, on a batch system or in the cloud.
Improve scaling and resource utilization using parallel execution and built-in scheduling. Speed up turnaround time with dependency awareness, plus a small memory footprint and real-time reporting that highlights failing jobs.
Achieve higher-quality results, standardize design flows and improve collaboration by reducing flow complexity. Work natively in any modern programming language and connect remotely to share mirrored views in real time.
Integrate with AI and machine learning workflows using popular development languages including Python, Julia, and Jupyter Notebook. Bring AI and machine learning services into traditional workflows and run a mix of both types of jobs, working alongside or on top of Kubernetes clusters. Flows dynamically adapt to resource availability and the results of previous calculations.
Control complex workflows on Windows or Linux x86/ARM platforms using a command line, web browser or full REST application programming interface (API). Choose from interactive, supervised and lights-out batch operating modes, and define flows using your preferred language. Transfer complete design flows, with tens of thousands of jobs and dependencies, in seconds with JSON-based Flow Definition Notation (FDN).
Manage dependencies between files and tools by capturing and updating flows and interacting with tools while they’re executing commands contained in the flows. Prevent jobs from executing until all prior dependency requirements are met, so users can quickly identify the root causes of failing jobs, take corrective action and resume execution from the points of failure without restarting.
