6395 - AI System Validation- Meta Perspective.pdf

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1、By:Carlos Fernandez HW Validation EngineerAI System Validation:Meta PerspectiveAI System Validation:Meta PerspectiveCarlos FernandezARTIFICIAL INTELLIGENCE(AI)Platform Overview(GT-Training)Overview:The Grand Teton system is designed with a modular and scalable architecture,allowing it to efficiently

2、 handle large-scale AI workloads.It typically includes a combination of CPUs,GPUs,and other accelerators interconnected to maximize performance.InterconnectsCPU to CPU:Efficiently managing distributed computing tasks and coordinating data processing across multiple CPU sockets.CPU to Accelerator:Off

3、loading parallelizable tasks like matrix multiplications or neural network computations to accelerators.Accelerator Interconnect:Enables direct communication between accelerators,bypassing the CPU for certain tasks to reduce latency.Data flow:Locally:Data blocks are processed by the CPU and then tra

4、nsferred to a switch,which routes the data to the appropriate GPU or accelerator via PCIe.Remotely:Data moves through a scale-out network,allowing it to be transferred to accelerators located in other hosts.System Topology Discussion PointsWorkloads,Stress,and Silent ErrorsAccelerator ComputeGEMM(Ge

5、neral Matrix Multiply)is a fundamental operation in linear algebra,involving the multiplication of two matrices.It is a core component of many scientific and engineering applications,including machine learning and vector mathematics.Matrix Sizing:The size of matrices involved in computations can imp

6、act the power consumption of a system.Larger matrices require more computational resources,leading to increased power usage.This can lead also to power capping and reduction in clocks,so optimizing the matrix size for validation purposes is critical.HBM(High Bandwidth Memory)used in high-performance