Heterogeneous computing is evolving to heterogeneous multiprocessing as of today.  We are no longer satisfied with acceleration with GPU alone but looking for dynamic allocation of resources from a mix of CPU, GPU, Many-Core, and FPGA in the same system. Will this evolution be driven by hardware, software, or users?  The evolution is real as the footprints are already existent like a big bang in high performance computing.  This article provides the key observations and a proposition to move forward.

Contents
1. Proposition
2. Footprints of Big Bang
3. Real Performance
4. Real Hardware
5. Real Software
6. Summary
7. References
8. Author/Contributors

1. Proposition

SKA (Square Kilometre Array) radio telescope1 is a gigantic science project by world standard and we are designing the instruments that will tell us more about where we came from and how we got here.  The data bandwidth required to handle the vast amount of signals from the sky for the tasks is as high as 7.9TB/s for the Survey telescope alone 2 (which is one of the 3 telescope systems in SKA).  Survey will be built in the desert of Western Australia beyond the economical reach of the current power grid.  Electricity for operating Survey is going to be expensive.  These 2 factors of data bandwidth and electricity cost are high on the agenda and need a different perspective of viewing high performance computing (HPC). 

New Zealand has been selected by the international SKA organisation and Central Signal Processing consortium to lead the design of Survey HPC.  The white paper2 from the New Zealand team obtained a high vote during the selection process for a global view that New Zealand has proposed to take.  SKA central signal processing is a specific task and needs a specific solution.  It is an early sign of things to come and the New Zealand team undertakes a generic or global computing view to finding the best fit for SKA.  The Government has endorsed our presence on the world stage 3 and provided funding to our team. 

Our design team members have proposed to carry out in-depth projects to exploit the potentials of new technologies that may emerge from the efforts of our team and our close collaborators from Canada, Australia, UK, and various member countries.   One of the projects being proposed by team members has defined 20 process milestones.

2. Footprints of a Big Bang

The International Exascale Software Project 4 started in 2010 has laid out the footprint of a big bang for addressing the big data issues facing the world over the next couple of decades.  The SAVE Project 5 under EU FP7 framework offers a specific direction.  In brief it pushes the philosophy of not one technology fitting all solutions.  It proposes deploying a mix of hardware technologies including CPU, GPU, Many-Core, and FPGA in one system to address computing tasks of various personalities.  Just-in-time dynamic allocation is an obvious method and this will require memory coherence of the various types of processors in the system.  Memory coherence is not a new topic as ARM has created AXI 6 and AMD has promoted the Heterogeneous System Architecture (HSA) 7 and proceeded to release its Kaveri APU at the end of 2013 implementing unified memory access and unified queues for CPU and GPU cores on the same die.

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• The IR LED has a finite strength to cover a pre-defined distance such as 20m within an angle of such as 50 degrees.  The IR beam could be too weak mostly or too strong sometimes. Too weak- the camera cannot capture or identify any object at night. Too strong- the object in question may be washed out in the video due to over-exposure. For example, a traditional IR LED camera may capture an outline of a person 10 meters away but when that person moves closer to the camera, the person shape may be washed out due to strong IR intensity.  We wish the IR LED can adjust its intensity by following the distance of the focused object. This capability requires some intelligence but if not all most cameras do not have it.

• Change the camera to one with zooming capability.  When the camera zooms in, it will automatically adjust the IR LED intensity to focus on the area that it zooms into. The IR beam will be concentrated in one area to narrow down the beam angle and extend the range to cover the zoom-in area. When zooming out, the camera will re-adjust the illumination back to cover the wider area, making sure that there is enough IR illumination in either near or far view.  This capability is called Adaptive Infrared.

• Compucon camera models CAB67 and CAB87 are among the first batch with this capability.

• Built in IR LED (without Adaptive capability)

• IR Filter Cut-off (to switch off the optical sensing capability in order to focus on IR sensing, and vice versa)

• Passive IR for detecting temperature of objects within a close distance (used for office security system).  An Compucon model is equipped with PIR.  

• IR Illuminators are external and not built inside the camera enclosure.  They provide much stronger IR beams than built-in type because there is no constraint imposed by the camera on heat generated and physical size.

With Adaptive Infrared	Without Adaptive Infrared