An advanced GPU management solution that maximizes user and task density, optimizing GPU utilization and performance across your data center.
Achieving peak GPU performance has eluded even the most advanced organizations due to the current limitations in managing and manipulating how data flows and threads execute on GPUs. These limitations have become a thing of the past with the introduction of ArcHPC - Nexus and Oracle.
Nexus is responsible for creating and managing your HPC environment while optimizing GPU utilization and performance. It enables administrators to enhance user and task density. Additionally, Nexus can manage multiple GPU models concurrently.
Oracle automates task matching and task deployment across your cluster. It manages low-level operational execution of instructions in your HPC environment and increases accelerated hardware performance through enterprise-wide scalable controls during run-time.
Optimized warp scheduling and thread arrangement, drastically improving task completion times.
Maintain processor uptime by keeping SMs "hot" by utilizing memory-level parallelism.
Integrates at the hypervisor level and is compatible with your current job scheduler.
Fine-tune your GPU task environment for minimum and maximum compute times at intersection points.
Match and deploy tasks automatically, maximizing task density and increasing GPU throughput.
Oracle automates task matching and deployment by analyzing machine code and latencies in your accelerated hardware architecture. This optimizes code deployment, ensuring tasks are executed in the most optimally tuned and calibrated HPC environments while factoring in user-defined governance aligned with business objectives across your entire HPC ecosystem.
Working concurrently with Oracle, Nexus manages your HPC environment's compute resources while communicating and executing live adjustments based on Oracle interactions. Oracle is effectively the brain that controls and automates task matching and deployment by user-defined governance and policies. Together, these solutions respond to dynamic changes during runtime while maximizing the utilization and performance thresholds of the underlying infrastructure.
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Even the most optimized code has latencies during memory access operations, resulting in missed opportunities to execute additional arithmetic operations, negatively impacting GPU performance. Current GPU management solutions cause slowdowns when revealing all compute resources to tasks running concurrently on the same hardware and are limited to splitting tasks/users across single GPUs.
|Manual Task Matching
|Easy of implementation/use
|Increases GPU performance
|Addresses latency issues
|Controls code optimization cycle
|No chance of performance degragation
|Granularly manages compute HPC environment
|Doesn't requires large investment in human capital
|Scalable across entire organization
|Highly secure, regardless of task strength
|Automated task matching
|Accessible to organizations of all sizes