http://techreport.com/onearticle.x/8887
Tech-Report報導了ATI與Stanford的Mike Houston合作的這次demo。
要點有二：
1. ATI將與Havok合作，開發物理引擎所需的相關API。
2. R520在GPGPU上的優勢，並且有幾個現成的demo
有一份相關的文件可以參考，並且包含了相關的測試結果。
http://graphics.stanford.edu/~mhous...20-mhouston.pdf
可以看得出來這些測試裡面X1800XT有相當的優勢。
引用:
GROMACS – GPU Implementation：
Written using Brook by non-graphics programmers
– Offloads force calculation to GPU (~80% of CPU time)
– Force calculation on X1800XT is ~3.5X a 3.0GHz P4
– Overall speed up on X1800XT is ~2.5X a 3.0GHz P4
Not yet optimized for X1800XT
– Using ps2b kernels, i.e. no looping
– Not making use of new scatter functionality
The revenge of Ahmdal’s law
– Force calculation no longer bottleneck (38% of runtime)
– Need to also accelerate data structure building (neighbor lists
• MUCH easier with scatter support
This looks like a very promising application for GPUs
– Combine CPU and GPU processing for a folding monster!
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話說這邊有個有趣的部份，就是Mike Houston倡導的部份：
引用:
What GPGPU needs from vendors More information
– Shader ISA
– Latency information
– GPGPU Programming guide (floating point)
• How to order code for ALU efficiency
• The “real” cost of all instructions
• Expected latencies of different types of memory fetches Direct access to the hardware
– GL/DX is not what we want to be using
• We don’t need state tracking
• Using graphics commands is odd for doing computation
• The graphics abstractions aren’t useful for us
– Better memory management Fast transfer to and from GPU
– Non-blocking Consistent graphics drivers
– Some optimizations for games hurt GPGPU performance
What GPGPU needs from the community
Data Parallel programming languages
– Lots of academic research
“GCC” for GPUs
Parallel data structures
More applications
– What will make the average user care about GPGPU?
– What can we make data parallel and run fast?
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這段的意思等於是說，GPGPU需要的資訊至少需要和CPU廠商提供出來的資訊同樣詳細；而以過往的經驗來說，這似乎會觸動到NVIDIA的神經....比方說光那個Shader ISA、Latency Information公開就已經觸動到NVIDIA的神經了吧。XD
所以短期內GPGPU這個範疇大概會是ATI比較佔優勢了。