Poplar^® Software Stack

Introducing Graphcore® Poplar® SDK Software

The Poplar SDK is a complete software stack, which was co-designed from scratch with the IPU, to implement Graphcore's graph toolchain in an easy to use and flexible software development environment.

At a high level, Poplar is fully integrated with standard machine learning frameworks so developers can port existing models easily, and get up and running out-of-the-box with new applications in a familiar environment.

Below these frameworks sits Poplar. For developers who want full control to exploit maximum performance from the IPU, Poplar enables direct IPU programming in Python and C++.

Multi-IPU Scaling & Communication

Poplar takes on the heavy lifting, so you don't have to, in a world of growing model sizes and complexity:

• High bandwidth IPU-Link™ communication, fully automated and managed by Poplar, treats multiple IPUs like a single IPU compute resource
• Graph Compile Domain (GCD) allows a single application to be programmed against multiple IPU processors, enabling both data parallel and model parallel execution
• Model sharding allows the simple splitting of applications across multiple devices
• Combining sharding with replication allows you to take code data parallel with minimum effort
• Advanced model pipelining lets users extract maximum system performance to run large models fast and efficiently

Framework Support

Poplar seamlessly integrates with standard machine intelligence frameworks:

• TensorFlow 1 & 2 support with full performant integration with TensorFlow XLA backend
• PyTorch support for targeting IPU using the PyTorch ATEN backend
• PopART™ (Poplar Advanced Runtime) for training & inference; supports Python/C++ model building plus ONNX model input
• Full support for PaddlePaddle and other frameworks is coming soon.

PopLibs™ Graph Libraries

PopLibs is a complete set of libraries, available as open source code, that support common machine learning primitives and building blocks:

• Over 50 optimised functions for common machine learning models
• More than 750 high performance compute elements
• Simple C++ graph building API
• Implement any application
• Full control flow support

Graph Compiler

Graphcore's state of the art compiler simplifies IPU programming by handling the scheduling and work partitioning of large parallel programs including memory control:

• Optimized execution of the entire application model to run efficiently on IPU platforms
• Alleviates the burden on developers to manage data or model parallelism
• Code generation using standard LLVM

Graph Engine

High performance Graph Runtime to execute models and stream data through models running on IPU:

• Highly optimized IPU data movement
• Interfaces to host memory system
• Device management: configuring the IPU-Link network, loading applications to devices & performing setup
• Debug and profiling capabilities