Engaget reports that the Kindle Fire HD has a PowerVR GPU inside which could in the future, be used for OpenCL accelerated graphics.
While Amazon has no immediate plans to implement OpenCL on the Fire HD, this Kindle Fire HD demo shows how OpenCL has the power to boost frame rates by 50 percent while simultaneously lowering power consumption by the same proportion.
This article by StreamComputing intends to help developers choose the best machine (price/performance) for OpenCL-development. It covers base memory, SSDs, motherboards, CPUs, and GPUs.
Conclusion & suggestions from the article
Before I give suggestions what to buy, this is the order of how I would spend my money on a new system:
Memory: fastest available, 16GB. SSD: fastest that could hold your data (or buffer it). Motherboard is where all the time will get in, but I'm sure it's worth it.
For the single GPU it is also an AMD, now NVIDIA backs out of “budget compute”. For dual-GPU I cannot make a good suggestion. Things might change when Intel releases Haswell in Q2 this year, and NVIDIA feels pressured and gets OpenCL back up.
Even though I have an Intel-processor in my main machine, I suggest to get an AMD processor. You get or 4 CPU-cores extra or a powerful embedded GPU. If you need PCIe 3.0, then Intel is the only choice.
Vivante announced that its multi-core GPUs have been widely adopted to power the next generation Advanced Driver Assistance Systems (ADAS) OpenCL Computer Vision applications from top tier automotive OEMs that combine inputs from cameras, sensors, GPS, RADAR, and IR to create “intelligent” cars that are very aware of their surroundings.
OpenCL ADAS calculations leverage parallel GPU SIMD engines to boost computation density by performing massively parallel data processing on multiple data streams simultaneously. Vivante GPGPU solutions are built around the just released OpenCL 1.2 Full Profile specification.
AMD has released the V1.0 of CodeXL, a unified developer tool suite that enables developers to quickly and easily identify performance issues and programming errors in applications, without requiring source code modifications.
CodeXL includes comprehensive GPU debugging, GPU and CPU profiling, static OpenCL kernel analysis and a standalone user interface on Windows and Linux for enhanced accessibility and navigation.
Highlights of AMD CodeXL v1.0 include:
For further information about CodeXL, visit the CodeXL homepage.
The new APP SDK 2.8 includes dozens of new and improved samples for OpenCL, Aparapi and C++ AMP that deliver significantly faster performance than APP SDK 2.7 – up to 2.3x faster on average in nine key benchmarks.
The APP SDK 2.8 also includes a preview version of AMD’s new open source C++ template library, codename “Bolt.”
Bolt is an STL compatible template library of data parallel primitives and provides a standard way to develop an application that can execute on either a regular CPU, or use any available OpenC™ capable accelerated compute unit, with a single code path.
V2.8 also SDK also improves and extends OpenCL capabilities by including support for the Direct3D 11 sharing Khronos extension in addition to including 64-bit atomics.
