The NVIDIA CUDA 4.0 Toolkit has been released. Supporting communications between the GPUs in the workstation will open the way for developing parallel applications using NVIDIA GPUs.
The new NVIDIA CUDA 4.0 Toolkit release has resulted in three main features:
# NVIDIA GPUDirect 2.0 Technology which offers support for peer-to-peer communication among GPUs within a single server or workstation. This enables fast multi-GPU programming and application performance.
# Unified Virtual Addressing (UVA) – Provides a single merged-memory address space for the main system memory and the GPU memories, enabling quicker and easier parallel programming.
# Thrust C++ Template Performance Primitives Libraries – Provides a collection of powerful open source C++ parallel algorithms and data structures that ease of programming for C++ developers. With Thrust, routines such as parallel sorting are 5X to 100X faster than with Standard Template Library (STL) and Threading Building Blocks (TBB).
"Unified virtual addressing and faster GPU-to-GPU communication makes it easier for developers to take advantage of the parallel computing capability of GPUs," said John Stone, Senior research programmer, University of Illinois, Urbana-Champaign.
"Having access to GPU computing through the standard template interface greatly increases productivity for a wide range of tasks, from simple cashflow generation to complex computations with Libor market models, variable annuities or CVA adjustments," said Peter Decrem, director of Rates Products at Quantifi. "The Thrust C++ library has lowered the barrier of entry significantly by taking care of low-level functionality like memory access and allocation, allowing the financial engineer to focus on algorithm development in a GPU-enhanced environment."
The CUDA 4.0 architecture release includes a number of other key features and capabilities, including:
# MPI Integration with CUDA Applications – Modified MPI implementations like OpenMPI automatically move data from and to the GPU memory over Infiniband when an application does an MPI send or receive call.
# Multi-thread Sharing of GPUs – Multiple CPU host threads can share contexts on a single GPU, making it easier to share a single GPU by multi-threaded applications.
# Multi-GPU Sharing by Single CPU Thread – A single CPU host thread can access all GPUs in a system. Developers can easily coordinate work across multiple GPUs for tasks such as “halo” exchange in applications.
# New NPP Image and Computer Vision Library – A rich set of image transformation operations that enable rapid development of imaging and computer vision applications.
# New and Improved Capabilities.
# Auto performance analysis in the Visual Profiler.
# New features in cuda-gdb and added support for MacOS.
# Added support for C++ features like new/delete and virtual functions.
# New GPU binary disassembler.
A release candidate of CUDA Toolkit 4.0 will be available free of charge beginning March 4, 2011, by enrolling in the CUDA Registered Developer Programme.Related links:NVIDIA CUDA 4.0