oneAPI Elements
oneAPI Implementations
Codeplay’s DPC++ Implementation Supports SYCL and other Languages for NVIDIA GPUs
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Tim Harris, Principal Architect
Azure AI, Microsoft
How oneAPI helped Microsoft Azure with its open standards-based initiatives
“The industry needs a programming model where developers can take advantage of an array of innovative hardware architectures. The goal of oneAPI is to provide increased choice of hardware vendors, processor architectures, and faster support of next-generation accelerators. Microsoft has been using oneAPI elements across the Intel hardware offerings as part of its initiatives and supports the open standards-based specification. We are excited to support our customers with choice and accelerate the growth of AI and machine learning.”
Languages
DPC++
Data parallel programming for CPU's and accelerators based on SYCL
hipSYCL++
Library-based implementation of SYCL
Numba
Compiler for data parallel programming in Python
DPCTL
Python bindings for SYCL classes
Julia
Compiler for data parallel programming in Julia
Deep Learning
TensorFlow
Deep learning framework
PyTorch
Deep learning framework
ONNX Runtime
Deep learning framework
Apache MXNet
Deep learning framework
PaddlePaddle
Deep learning framework
oneCCL
Communication primitives for distributed deep learning
oneDNN
Computation primitives for distributed deep learning
Data Science
Modin
Accelerated Pandas
Scikit-learn-intelex
Accelerated Scikit-learn
oneDAL
Accelerated machine learning algorithms for C++, Python, and Java
Video, Ray Tracing, and Rendering
Libraries
dpNP
NumPy-like API accelerated with SYCL
Level Zero
Low-level runtime for oneAPI
MPICH
High-performance implementation of MPI
oneDPL
Library for implementing data parallel algorithms with DPC++
oneMKL
Math kernel library
oneTBB
Threading building blocks
oneVPL
Decoding, encoding, and processing
Tools
HPCToolkit
Profiling toolkit from Rice University