Performance Engineering Platform
Install on your GPU server and let it gather runtime data from your AI workloads. It identifies bottlenecks, automatically enhances performance, and gives you detailed insights to achieve even greater manual optimization.
Whether in the cloud or on-premises, simply install Fixstars AIBooster on your GPU servers to gather detailed performance data from active AI workloads, visualizing bottlenecks clearly.
We provide the ZenithTune library, which helps you achieve peak performance with minimal coding, unlocking your application's full potential.
Challenges in Accelerating Deep Learning Model Inference on NVIDIA GPUs
AcuiRT fully automates the conversion of AI models built with PyTorch into TensorRT. It dramatically reduces development time and boosts inference speed without requiring specialized expertise.
Performance is not constant—it evolves due to new model adoption, parameter changes, and infrastructure updates. By continuously running the performance improvement cycle, you can prevent degradation and always achieve peak performance.
Note: These results include both automatic accelerations by Fixstars AIBooster and additional manual accelerates based on collected performance data.
AIBooster consists of two main components:
AIBooster Agent
The Agent is a Linux application that you install on the GPU compute nodes you
manage. It collects performance data from each node and sends it to the Server. It doesn't matter whether
the compute nodes are on the cloud or on-premises.
AIBooster Server
The Server stores the received data and provides a dashboard for easy data
visualization. By simply accessing the dashboard from your browser, you can monitor the performance of each
compute node.
There are two ways to use the AIBooster Server:
AIBooster supports multi-cloud environments and server clusters distributed across multiple locations. From a single dashboard, you can view the status of your entire system, detailed information for each node, and even detailed information for each compute job.
You will install the AIBooster Agent on each GPU compute node.
The management dashboard is provided as a web application on the cloud, managed by Fixstars.
To begin, you will create an account and enter your user information on the management screen. A dedicated
URL will be issued for you to access the dashboard through your browser.
You will designate one management node and install the AIBooster Server on it, and then install the
AIBooster Agent on each GPU compute node.
From your personal computer, you can view the dashboard provided by the management node through a browser
via TCP port 3000.
This is the recommended configuration for most GPU cluster server systems.
The software runs as a Linux daemon, meaning it's always active with minimal overhead. We refer to it as having "near-zero overhead."
It runs on Debian-based Linux environments. We have verified operation on Ubuntu 22.04 LTS. It can also run without an NVIDIA GPU, but the available data and functionality will be limited.
Fixstars AIBooster is free to use. However, the Performance Intelligence (PI) feature is available at no cost for the first month after activation and becomes a paid feature thereafter. Please refer to the Fixstars AIBooster's End User License Agreement for details.
Fixstars does not collect user-specific data (such as your application data or detailed analysis results). We only gather general usage statistics for product improvement purposes. Contact us for more details.
Traditional tools (e.g., DataDog, NewRelic) show hardware utilization, but Fixstars AIBooster additionally captures detailed AI workload data. It analyzes this data to identify and resolve performance bottlenecks.
It optimizes performance by analyzing data from Performance Observability (PO). This includes changing infrastructure configurations, tuning parameters, and optimizing source code to maximize GPU utilization.
Profiling tools (like NVIDIA Nsight) capture "snapshots" triggered by specific commands. In contrast, AIBooster continuously captures detailed performance data, enabling historical analysis and identification of performance degradation. AIBooster’s automatic acceleration suggestions and implementations are unique features.
Yes. Because the underlying technology is broadly applicable, other AI or GPU-accelerated workloads can also benefit. The exact improvements depend on your specific workload—please contact us for details.
Detect hidden bottlenecks and automatically accelerate your AI workloads.
Achieve further acceleration manually by utilizing acquired performance data.
