Date: 15th Jun 2011
AMD launched software tools to optimize
apps for OpenCL standards
AMD has announced a new set of software development tools
and solutions to optimize their applications for OpenCL
standards so that developers can develop systems based on
AMD's new Fusion Family of Accelerated Processing Units
(APUs) faster.
"AMD is working closely with the developer community
to make it easier to bring the benefits of heterogeneous
computing to consumers, enabling next-generation system
features like vivid video, supercomputer-like performance
and enhanced battery life," said Manju Hegde, corporate
vice president, AMD Fusion Experience Program. "Our
advanced developer tools and solutions enable a new era
of parallel programming that's based on industry standards
and focused on delivering innovative user experiences that
span a variety of computing form factors."
Among the new offerings is the gDEBugger product, which
was created by experts from AMD's new Israeli research center,
based on AMD's acquisition of startup company Graphic Remedy
in October 2010. gDEBugger is an advanced OpenCL and OpenGL
debugger, profiler and memory analyzer. The new AMD gDEBugger
release provides developers with the ability to debug OpenCL
kernels, running on AMD GPUs, and step through their source
code while examining kernel variables and data. This product,
which is a plug-in designed to work with Microsoft Visual
Studio, includes all of gDEBugger's previous features and
capabilities.
Additional developer solutions include a Parallel Path
Analyzer (PPA), Global Memory for Accelerators (GMAC) and
Task Manager tools, which are being developed by Multicoreware
in collaboration with AMD. These new tools and solutions,
expected to be available in Beta during Q3 of this year,
are designed to make OpenCL GPU development easier and more
efficient.
Parallel Path Analyzer (PPA) is an advanced profiling tool
for developing applications that optimize both GPU and CPU
load. The PPA visualizes data transfers and kernel execution,
identifies system-wide critical paths and locates data dependencies.
The Global Memory for Accelerators (GMAC) API provides a
framework in which a developer can create applications leveraging
the immense compute capabilities of OpenCL, but without
the overhead of having to explicitly manage multiple data
buffers across the separate address spaces of GPU and CPU.
The Task Manager API provides a framework for managing compute
tasks in a heterogeneous multi-core environment. OpenCL
kernels can be automatically scheduled to execute on an
available and task-appropriate device, providing dynamic
load balancing, optimizing use of available compute resources
and removing the burden of explicit schedule handling.
|