Concurrent’s GPU Workbench™ offers the ideal solution for applications that require massive parallel processing. GPU Workbench is a complete platform for developing and deploying real-time applications using NVIDIA CUDA technology.
GPU Workbench is the perfect tool to help scientists and engineers manage compute-intensive processes in a wide variety of fields including molecular biology, cosmology, particle physics, radar and sonar data analysis, economics and medical imaging. GPUs provide a very cost-effective solution for parallel processing applications. With GPU Workbench, you can execute processes on a GPU in just a fraction of the time needed to run them on a CPU.
Based on the latest available commercial GPU and CPU products, Concurrent’s powerful GPU Workbench platforms are powered by our deterministic RedHawk Linux, specially optimized for real-time CUDA performance. See our whitepaper below on Improving Real-Time Performance With CUDA Persistent Threads (CuPer) on Jetson TX2.
At the heart of each GPU Workbench solution is Concurrent’s RedHawk Linux real-time operating system. Compatible with the popular Red Hat and CentOS distributions, RedHawk provides high I/O throughput, fast response to external events, optimized interprocess communication and NUMA memory management. RedHawk is the ideal Linux environment for complex real-time CUDA applications.
Proprietary GPU drivers supplied by NVIDIA frequently make demands upon kernel resources that can dramatically impact real-time performance. RedHawk addresses these special challenges and guarantees the performance of real-time processes when CUDA processes are concurrently running on a system.
RedHawk Linux, which includes the latest version of the NVIDIA CUDA SDK, reduces the process dispatch latency in CUDA applications from hundreds of microseconds to under 10 microseconds. RedHawk minimizes jitter and provides better overall performance.
GPU Workbench supports Concurrent’s powerful NightStar development tool kit. Users can debug, analyze, monitor, schedule and tune their real-time GPU applications non-intrusively, thus preserving the execution behavior of the real-time application. NightStar includes an application illumination feature allows programmers to automatically trace CUDA API function calls and examine the values of parameters passed and returned as well as get a detailed timing analysis of CUDA kernel executions.
NightStar also permits a user to graphically view the interaction between the Linux kernel and multiple application threads in real-time. NightStar allows users to add trace points into the CUDA kernels that are executed by the GPU.
Concurrent offers fully customized real-time GPU Workbench platforms that can contain up to eight of the latest NVIDIA Tesla and graphics cards and up to four CPU sockets. Systems come in standard tower, rackmount or desktop enclosures with up to 12 integral PCIe slots and optional expansion chassis. Configurations can contain up to 24 disk drives with optional RAID. Multiple GPU Workbench systems can be interconnected using 10 Gbit Ethernet or high-speed fabrics.