Stephen Pawlowski of Intel gave an interesting keynote today at ISC 2013. He continued the theme of yesterday’s keynote to address challenges our market faces in getting to exascale computing. Here is a summary of the points he made during his talk:

  • Getting to exascale by 2020 requires performance improvement of 2x every year
  • Innovations anticipated include stacked chips and optical layers
  • DRAM is not scaling with Moore’s Law
  • More power goes into transferring data than in computing
  • Need to operate transistors near threshold
  • New materials for DRAM needed. Resistive memory could replace DRAM.
  • Need to explore both the big die and the small die paths as we approach 2020
    • Big die path leads to 10 billion transistors on a die
    • Small die path entails fewer transistors on a small cost effective die

It is clear that Intel is thinking hard about the challenges facing the processor industry and exploring many simultaneous paths to push the market forward.

We look forward to finding the video and slides of Stephen Pawlowski’s keynote posted online, and we’ll provide a link to that information when it is posted by Intel and the ISC 2013 coordinators.

If you are at ISC 2013, you can find us demoing in NVIDIA’s booth #220 near the exhibition entryway.

 

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Bill Dally of NVIDIA gave a wonderful keynote today at ISC 2013. He focused on addressing the challenges facing our market in getting to exascale computing.

He talked about how Moore’s law is alive and well because transistors continue to double at an astonishing rate. However, the additional transistors are not translating into the same big performance gains as they did in the 1990’s. Whereas performance used to grow 50% per year, performance today is growing at a much slower pace.

The biggest bottleneck to more performance is energy efficiency. Bill showed slides of chips and talked about the picojoules required to compute versus those required to move data and operands around the chip. The take home message was that communication across modern processors takes much more power than arithmetic calculations.

In order to get to exascale, we need processors that are 25x more energy efficient than those available today. Only 10% of that performance boost is expected to come from process improvements in silicon technology between now and 2020. The rest will have to come from improvements in circuits and architectures, mainly focused on reducing the power required to communicate data across the chip.

Bill also spent time discussing the software side of HPC. He showed a diagram depicting the interplay between programmers, tool vendors, and architecture teams. He talked about how those 3 groups need to play well together, like kids on a soccer team, in order to move the market forward. Programmers need to rely on tools and focus on their science rather than wasting time re-inventing wheels. Architecture teams need to expose enough interface to the tool developers so that efficient tools can be made. Tools teams need to focus on quality and build trust with programmers.

These are all things we focus on at AccelerEyes with ArrayFire. We spend an enormous amount of manpower in quality testing our software and in working with our users to prioritize features for upcoming ArrayFire releases. Doing so enables us to build useful functions to accelerate science, engineering, and finance codes.

We look forward to finding the video and slides of Bill Dally’s keynote posted online, and we’ll provide a link to that information when it is posted by NVIDIA and the ISC 2013 coordinators.

If you are at ISC 2013, you can find us demoing in NVIDIA’s booth #220 near the exhibition entryway.

 

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ArrayFire Examples (Part 6 of 8) – Multiple GPUs

June 12, 2013

This is the sixth in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the multi_gpu/ directory. In these examples, my machine has the following configuration:

*The following order represents the speed of GPUs in [...]

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ArrayFire Examples (Part 5 of 8) – Machine Learning

June 5, 2013

This is the fifth in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the machine_learning/ directory. In these examples, my machine has the following configuration:

   1. K-Means Clustering - kmeans.cpp Figure 1 This is [...]

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Solution to NVIDIA Toolkit Installation Error for Ubuntu 12.10
[Driver: Installation Failed]

May 20, 2013

  You may find this error message while trying to set up the NVIDIA CUDA Toolkit in Ubuntu. I found it when I was installing the toolkit for ArrayFire   [1] CUDA Toolkit Installation 1. Download the CUDA Toolkit in the link.  2. Extract the .run file in a location

  3. Exit the [...]

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Beamforming with ArrayFire

May 7, 2013

Alessandro Savoia and researchers at Università degli Studi Roma Tre have achieved an order of magnitude improvement in the performance of a beamforming application using ArrayFire for GPU acceleration with CUDA-capable NVIDIA GPUs. This application involves conventional beamforming. Steps include the application of a time delay to each signal vector, summation across all vectors, and processing on the [...]

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Are You Getting Left Behind?

May 1, 2013

HPCwire posted a nice article today with trends from IDC on computer processing. These trends fall inline and corroborate things we’ve been saying here on this blog. Accelerators (including GPUs and co-processors) are taking off. Are you getting left behind? If you’re reading this blog, you’re probably at the bleeding edge, but nonetheless here are [...]

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History of the Modern GPU Series

April 23, 2013

Graham Singer over at Techspot posted a series of articles a few weeks ago covering the history of the modern GPU. It is well-written and in-depth. For GPU affectionados, this is a nice read. There are 4 parts to the series: Part 1: (1976 – 1995) The Early Days of 3D Consumer Graphics Part 2: (1995 – 1999) 3Dfx Voodoo: [...]

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ArrayFire Examples (Part 4 of 8) – Image Processing

April 16, 2013

This is the fourth in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the image_processing/ directory. In these examples, my machine has the following configuration:

Image Demo The purpose of this example is to [...]

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ArrayFire + Scorpii Demo by CreativeC

April 10, 2013

CreativeC makes awesome compute + visualization systems. We got to see the demo in live action at the GPU Technology Conference last month. Tim Thomas was kind enough to let us film the demo showing how ArrayFire can be used to drive a multi-node, 9 GPU system in a physics application. Checkout the video below. [...]

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