Dynamic Heterogeneous Compute Relocation for AI Workloads
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Solution Overview
Problem
High-performance computing (HPC) and AI workloads face challenges in fully utilizing complex heterogeneous compute resources and optimizing performance across different architectures, leading to underutilization and suboptimal resource allocation.
Innovation Solution
Identifying significant sections of code within workloads using hardware and software techniques, such as annotations, run-time profiling, and performance counters, to dynamically relocate compute and communication portions across various heterogeneous compute resources, predicting and optimizing their placement for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If code sections are statically assigned to specific compute resources, then implementation simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic code relocation by monitoring runtime performance metrics and automatically migrating code sections between different compute resources (CPU, GPU, FPGA) based on current system state and workload characteristics, transforming static assignments into adaptive dynamic placements that optimize resource utilization
Solution Approach 2:
The system continuously monitors performance counters and runtime metrics from heterogeneous compute resources, uses this feedback to identify underutilized resources or performance bottlenecks, and automatically relocates code sections to optimize overall system throughput and resource efficiency
2Adaptability or versatility
If compute resources are heterogenized to solve diverse workloads, then versatility is improved, but performance optimization difficulty increases
Solution Approach 1:
The system automatically profiles workload characteristics, identifies suitable compute resources, and performs code section relocation without manual intervention, enabling the heterogeneous system to self-optimize for different workload types and eliminating the need for manual performance tuning
Solution Approach 2:
The system dynamically changes operational parameters by monitoring performance metrics and automatically adjusting code placement decisions based on runtime conditions, transforming the static configuration into an adaptive system that optimizes for different workload characteristics
3Productivity
If code sections are relocated to optimize performance, then resource utilization is improved, but data transfer overhead increases
Solution Approach 1:
The system performs preliminary profiling and analysis to identify code sections that are candidates for relocation, evaluates potential target resources beforehand, and only initiates data transfer when the performance benefit outweighs the transfer cost, preventing unnecessary migrations
Data Source
AI summary
Embodiments described herein are generally directed to improving performance of high-performance computing (HPC) or artificial intelligence (AI) workloads on cluster computer systems. According to one embodiment, a section of a high-performance computing (HPC) or artificial intelligence (AI) workload executing on a cluster computer system is identified as significant to a figure of merit (FOM) of the workload. An alternate placement among multiple heterogeneous compute resources of a node of the cluster computer system is determined for a portion of the section currently executing on a given compute resource of the multiple heterogeneous compute resources. After predicting an improvement to the FOM based on the alternate placement, the portion is relocated to the alternate placement.