InAccel has developed an integrated machine-learning (ML) set of tools called the Accelerated ML suite that delivers as much as a 7x performance increase for applications such as logistic regression, K-means clustering, and gradient-boosted trees (GBTs) using XGBoost by leveraging FPGA acceleration available through the Alibaba Cloud’s f1 instance, which is based on Intel® Xeon® processors and Intel Arria® 10 FPGAs. The Accelerated ML suite IP cores for these workloads are instantiated on Intel Arria 10 FPGAs in the Alibaba Cloud f1 instances.
According to InAccel, its Accelerated ML suite offers more than a 7x kernel performance improvement for ML training on logistic regression and a 5x overall system performance, which includes the time to transfer data and the CPU-based data preprocessing. For K-means clustering, the InAccel IP cores provide a 6.4x kernel performance improvement for ML training and an overall 4.3x system performance improvement when including the data preprocessing and the communications between the Alibaba Cloud’s host Intel Xeon CPUs and the Intel Arria 10 FPGAs in the Alibaba Cloud f1 instance.
The net result is faster ML model training.
InAccel also provides an FPGA resource manager that allows you to scale the performance of these applications by using multiple FPGAs per server and multiple servers with FPGAs. InAccel’s FPGA resource manager also allows you to share the Alibaba Cloud f1 instance’s FPGA resources among multiple applications using virtualization.
Additional details are in the just-published InAccel blog.
- For more information about the Accelerated ML suite, please contact InAccel
- For more information about FPGA-based workload acceleration, see the Intel FPGA Acceleration Hub.
- For additional information about the FPGA-accelerated Alibaba cloud f1 instance, see “Intel FPGAs Power Acceleration-as-a-Service for Alibaba Cloud.”
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