GPU-Centric Attestation for Confidential Compute Mode Assessment
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Solution Overview
Problem
Current graphics processing units (GPUs) face challenges in efficiently processing graphics data due to the limitations of fixed function computational units and the need for improved parallel processing techniques, particularly in SIMT architectures, which can lead to inefficiencies in executing program instructions.
Innovation Solution
Implementing a GPU-centric assessment of confidential compute mode using attestation to enhance security and performance by ensuring secure I/O operations through Trusted Execution Environment (TEE)-I/O support, enabling trusted I/O virtualization and secure partitions within the GPU architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fixed function computational units are used in GPUs, then hardware implementation is simplified, but processing versatility and adaptability are limited
Solution Approach 1:
The GPU architecture is divided into multiple independent streaming multiprocessors (SMs), each capable of executing different program instructions simultaneously. This segmentation allows the system to maintain simple fixed-function computational units within each SM while achieving overall processing versatility through the collection of diverse SMs handling different workloads.
Solution Approach 2:
The streaming multiprocessors are designed with universal functional capabilities that can be configured through software to perform various processing tasks. Each SM contains configurable functional units that can be programmed via CUDA or other GPU programming models, enabling the same hardware structure to serve multiple processing purposes including graphics rendering, scientific computing, and machine learning.
2Productivity
If SIMT architecture with synchronous thread execution is implemented, then parallel processing efficiency is improved, but instruction execution flexibility is reduced
Solution Approach 1:
The SIMT architecture implements dynamic thread scheduling where the GPU controller can adjust thread execution patterns based on workload characteristics. Threads can be organized into warps or wavefronts with configurable sizes, and the system can dynamically switch between synchronous execution for coalesced memory access and more flexible execution patterns for divergent workloads, balancing efficiency and flexibility.
Solution Approach 2:
The architecture launches excessive threads beyond what is strictly necessary for the current task, allowing the hardware to maintain full utilization through synchronous execution while software selectively activates or deactivates thread groups. This approach preserves the simplicity of synchronous SIMT execution while providing flexibility through selective thread group management.
3Reliability
If secure partitions and TEE-I/O support are added to GPU architecture, then security and trustworthiness are enhanced, but system complexity increases
Solution Approach 1:
The GPU system is segmented into secure and non-secure domains through separate address spaces and memory regions. Secure partitions are isolated with dedicated resource pools, and TEE-I/O operations are handled through separate secure channels. This segmentation provides robust security isolation while keeping each domain's internal complexity manageable and well-defined.
Solution Approach 2:
A secure monitor or trust manager acts as an intermediary between secure and non-secure components, mediating access requests and enforcing security policies. This intermediary layer handles the complexity of security management centrally, allowing individual GPU components to remain relatively simple while the system as a whole achieves high security through the mediating security infrastructure.
Data Source
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AI summary
An apparatus to facilitate GPU-centric assessment of confidential compute mode using attestation is disclosed. The apparatus includes processing cores of a graphics processor communicably coupled to a baseboard management controller (BMC), the one or more processing cores to: cause the BMC to request CPU attestation evidence from a workload (WL) attesting environment of a central processing unit (CPU) that is communicably coupled to the graphics processor; select a graphics confidential compute (CC) mode of the graphics processor; communicate the graphics CC mode to a tenant management console orchestrator (TMCO) of the CPU; receive, by an attestation verifier of the processing cores, the CPU attestation evidence comprising CPU partition evidence of a CPU partition, the CPU partition evidence collected by the WL attesting environment of the CPU; and verify, by the attestation verifier, the CPU partition evidence and identify a CPU partition CC mode based on the CPU partition evidence.