Explore a conference talk from USENIX ATC '20 that introduces offload annotations (OAs), a novel approach to enable heterogeneous GPU computing in existing workloads with minimal code modifications. Learn how OAs allow developers to leverage specialized hardware accelerators like GPUs through high-level APIs, addressing challenges in kernel library development and adoption. Discover how an annotator marks CPU library types and functions with equivalent kernel library functions, providing an offloading API for input/output partitioning and device memory management. Understand the runtime's role in mapping CPU functions to GPU kernels and managing execution, data transfers, and paging. Examine the significant performance improvements achieved in data science workloads using CPU libraries like NumPy and Pandas, with speedups of up to 1200x and a median of 6.3x through transparent GPU offloading. Gain insights into OAs' ability to match handwritten heterogeneous implementations and automatically handle data paging for datasets larger than GPU memory.
Offload Annotations - Bringing Heterogeneous Computing to Existing Libraries and Workloads
Overview
Syllabus
Intro
Background: Hardware Commoditization
Background: CPUs vs. GPUs
Background: Data Science on the CPU
Trend: Data Science on the GPU
Trend: CPU Libraries vs. GPU Libraries
Goals
Annotator - Function Annotations
Step 1: Annotator - Allocation Annotations
Step 1: Annotator - Offload Split Type
End User
Step 3: Runtime - Offloading API
Step 3: Runtime - Splitting API
Step 3: Runtime - Scheduling Heuristics (optional)
Integration Experience
Evaluation: Summary
In-Depth Evaluation: Allocations
In-Depth Evaluation: Heuristics
In-Depth Evaluation: Splitting/Paging Datasets
Conclusion
Taught by
USENIX
