סיפורו המדהים של מעבד ה ARM הראשון בעולם והממציאה ראיון ראשון ובלעדי ב ווידאו עבור הקהילה

זהו סיפורו של מעבד ARM והממיציאה של מעבד ה ARM גברת סופי ווילסון
ראיון מיוחד בווידאו עבור הקהילה באמצעות ARMDEVICE.NET
תודה ל CHARBAX
הראיון הוא בשפה האנגלית .
הראיון נעשה ב חלקים
חלק 1


חלק 2

Sophie Wilson designed the instruction set for the original ARM Processor (Acorn RISC Machine) in 1983-1985 for the Acorn Computers which she designed in the 70's, 80's and 90's.

I interview Sophie Wilson at the Computing History Museum in Cambridge [You must be registered and logged in to see this link.]

Check back for one more hour of Interviewing with Sophie Wilson to be posted on [You must be registered and logged in to see this link.] in the days to come!












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שימו לב לדגבר הבא :
שרת עם מעבד 48 ליבות
48-core 64bit Cavium ThunderX ARM Server demonstrating Virtualization

Cavium is showing the most powerful ARM Processor in the world, with a 48-core ARMv8 64bit processor, demonstrating the high-performance visualization running the Xen Hypervisor running on an internal evaluation board and the KVM Hypervisor running on a rack-mounted 1U platform.


שרת עם מעבד 48 ליבות
Cavium ThunderX 48 Core 2.5Ghz ARM Server SoC



Clusterbit ARM Linux cluster



מעבד 64 ליבוד ARM




Allwinner A64 quad-core ARM Cortex-A53, 64bit Tablets run Android 5 Lollipop


Allwinner shows their first 64bit Tablets, running the Allwinner A64 quad-core ARM Cortex-A53 running Android 5 Lollipop. Allwinner positions this solution as the cheapest 64bit Tablet solution, a step-up from the 32bit sub-$4 Allwinner A33, planning mass production for June 2015, here showing it running in a 9.6" 1280x800 tablet. Featuring 9.9 second fast boot, HDMI, improved smart color, 4K video playback, H265 hardware decode.

מדהים שהיא המציאה את זה .
הייתי בטוח שזה קבוצה ואיזה גאון ופתאום מגלים שזה בכלל איזה .
הפתעה של החיים.
בלי הסינים אף אחד לא היה שומע על המעבד הזה .
אם תקדמו את המעבד אז זה יצליח עוד יותר.
Raspberry Pi cluster(16nodes) making



Raspberry Pi B+ Cluster (Super Computer) Part 1


Raspberry Pi B+ Cluster (Super Computer) Part 2


Raspberry Pi B+ Cluster (Super Computer) Part 3



Raspberry Pi B+ Cluster (Super Computer) Part 4



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Raspberry Pi B+ [You must be registered and logged in to see this link.]

GeauxRobot Raspberry Pi B+ Stackable Case: [You must be registered and logged in to see this link.]

SanDisk 8GB microSD [You must be registered and logged in to see this link.]

NETGEAR 5-Port Switch [You must be registered and logged in to see this link.]

Anker [3-Pack] Premium 3ft Micro USB [You must be registered and logged in to see this link.]

Amazon USB Hub [You must be registered and logged in to see this link.]

Belkin RJ45 CAT 5e 3ft Cable [You must be registered and logged in to see this link.]

Brought to you by [You must be registered and logged in to see this link.]

מי היה מאמין שאחת המפתחות הראשיות היא בעצם אישה בריטית.
הסינים לוקחים כל מה שבזול .
אבל צריך להודות שאם הסינים לא היו משתמשים בזה בצורה מאסיבית.
יתכן ולא היו למעבד הזה חיים ארוכים ומוצלחים כל כך.
יש כבר שרתים שמבוססים על מעבד ARM למי שלא יודע.
אבל בסך הכול היא הייתה חלק מהמפתחים והיא לא הממציאה.
למרות מה שהיא טוענת .
ב BBC הראו את כל אלה שפתחו אולי היא הייתה הראשונה הראשית שבהם
אבל צריך לתת לה קרידט .
הכוח הנשי .
אז מי אמר שנשים לא חכמות ולא מבינות בתוכנות בפיתוח .
בשנים האחרונות רוב הדברים הכי גדולים בטכנולוגיה העלית נעשו על ידי נשים.
לא רק שהם מנהלות יותר טוב מגברים.
הם מצליחות יותר הלכה למעשה .
והדבר הכי חושב לאישה יש מוח יותר גדול מהגבר
זה מוכח מדעית .
אז בתכלס היא חכמה יותר מכל גבר.
מי שלוט בכל בית אם לא האישה ?

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The RPiCluster


Documentation, Source code, and EagleCAD designs: [You must be registered and logged in to see this link.]

Summary:
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The RPiCluster is a 33 node Beowulf cluster built using Raspberry Pis (RPis). During my dissertation work at Boise State University I had need of a cluster to run a distributed simulation I've been developing. The RPiCluster is the result. Each of the 33 RPi is overclocked to 1GHz and is running Arch Linux. This demo shows the RPiCluster running a parallel program I developed using MPI to control all of the RGB LEDs installed on each of the nodes.

The Whole Story:
The RPiCluster project was started in Spring 2013 in response to a need during my PhD dissertation research. My research is currently focused on developing a novel data sharing system for wireless sensor networks to facilitate in-network collaborative processing of sensor data. In the process of developing this system it became clear that perhaps the most expedient way to test many of the ideas was to create a distributed simulation rather than developing directly on the final target embedded hardware. Thus, I began developing a distributed simulation in which each simulation node would behave like a wireless sensor node (along with inherent communications limitations), and as such, interact with all other simulation nodes within a LAN. This approach provided true asynchronous behavior and actual network communication between nodes which enabled better emulation of real wireless sensor network behavior.

So, why I would want to build a Beowulf cluster using Raspberry Pis? The Raspberry Pi has a relatively slow CPU by modern standards. It has limited RAM, slow USB-based 10/100 Ethernet, and its operating system runs directly on a SD card. None of these "features" are ideal for a cluster computer! Well, there are several reasons. First, when your dissertation work requires the use of a cluster it is nice to ensure that there is one available all the time. Second, RPis provide a unique feature in that they have external low-level hardware interfaces for embedded systems use, such as I2 C, SPI, UART, and GPIO. This is very useful to electrical engineers (like myself) requiring testing of embedded hardware on a large scale. Third, having user-only access to a cluster (which is the case for most student-accessible systems) is fine if the cluster has all the necessary tools installed. If not however, you must then work with the cluster administrator to get things working. Thus, by building my own cluster I could directly outfit it with anything I might need. Finally, RPis are cheap! The RPi platform has to be one of the cheapest ways to create a cluster of 32 nodes. The cost for an RPi with an 8GB SD card is ~$45. For comparison, each node in one of the clusters available to students here at BSU, was about $1,250. So, for not much more than the price of one PC-based node, I could create a 32 node Raspberry Pi cluster!

Update: While the BeagleBone Black was not available when I started this project, I would have chosen it rather than the Raspberry Pi had it been available. It is the same cost once you include an SD card, but it has onboard 2GB of flash storage for the operating system. It also uses a Cortex-A8 ARM processor running at 1GHz.

Cluster Performance:
I measured basic computing performance in a number of ways (see the paper). MPI performance was measured using HPL (http://www.netlib.org/benchmark/hpl/). The RPiCluster achieved 10+ GFLOPS peak, with 32-nodes running HPL. The single 3.1GHz Xeon E3-1225 (quad-core) system, I used for comparison, showed about 40 GFLOPS peak (when the HPL problem was optimized for Xeon system).

When I run the HPL problem that achieves 10 GFLOPS on the RPiCluster, the Xeon system achieves about 2 GFLOPS. This is because the HPL problem size is so large that it causes paging on the Xeon system. The Xeon system has 8GB of RAM (~6GB usable after OS, etc) whereas the RPiCluster has about 16GB of RAM (~15GB usable after OS, etc).

More information: [You must be registered and logged in to see this link.]

Update:
I finished my PhD Spring 2014. For those interested in further details on what I was doing, an electronic copy of my dissertation is available here: [You must be registered and logged in to see this link.]

אם אתם מעלים את זה זה סימן שאתם הוכלים לפתח משהו מבוסס ARM.
השאלה למה לא עשיתם עד עכשיו ?
מי שרוצה לפתח אפשר לקנות רכיבים מEBAY כולל לוח פיתוח בגרושים

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World's Most Expensive Hard Drive Teardown 1970
10MB דבר כזה לא ראיתם מעולם חובה לצפות משהו עצבני במיוחד