Heterogenous Computing

Transcription

1 Heterogenous Computing Fall 2018 CS, SE - Freshman Seminar 11:00 a 11:50a

2 Computer Architecture What are the components of a computer? How do these components work together to perform computations? How do we instruct computers how and what to compute? 1

3 Computer Architecture Can we understand this computation process in a general sense? That is, can we express computation for all computers or at least a large class of computers? 2

4 Computer Architecture 3

5 Computer Architecture 4

6 Computer Architecture Can we instruct computers how and what to compute in a general sense? That is, can we write software in a high-level language that is generative in terms of producing solutions? 5

7 Computer Architecture 6

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9 Computer Architecture 8

10 What about this question? Which processor do we use? Is this the same question as which architecture do we use? Are there different processors particularly engineered for specific problems? Can you think of one example right now that is likely in your computer or laptop and possibly phone or tablet? 9

11 What about this question? Which processor do we use? Is this the same question as which architecture do we use? No not the same question - Architecture specifically refers to the compute system components and their interaction lots of these SIMD, MIMD, SISD Are there different processors particularly engineered for specific problems? Can you think of one example right now that is likely in your computer or laptop and possibly phone or tablet? GPUs, Communications Chips (Wifi, Ethernet, Bluetooth), etc. 10

12 Why do we have a CPU and a GPU? What are the advantages? What do they do differently? How are they controlled (programmed)? What kinds of problems do each solve? 11

13 What is computer performance? A measure of how quickly a machine can process instructions per unit time. What governs this rate? Is this rate bounded or can we make processors as fast as we want? 12

14 Gordon Moore Moore s Law Moore observed that the number of components (transistors, resistors, diodes or capacitors) in a dense integrated circuit had doubled approximately every year, and speculated that it would continue to do so for at least the next ten years. (4/19/1965) Revised the rate to approximately every two years. (revised 1975) What a fantastic outcome that computer power is effectively doubling every two years 13

15 The Power Wall and Multiple Core Designs First, this is not Tesla s Power Wall, but the power wall concept associated with computer architecture The doubling of component density on integrated circuits to scale up the performance, also increased (exponentially) the power consumed to switch the transistors and to cool the chips. To address this issue, chip manufacturers began experimenting with replicating the computing components of the chips and layering these in different ways to provide better management of heat, and reuse chip components in the packaging. We ended up with multiple compute cores in our modern day chips. 14

16 Multicore as a scalar performance increase Multiple compute cores means the introduction of multiple instruction streams can be computed at once Can compute four instruction streams in the time it took to compute a single instruction stream if the CPU has four compute cores Linear scaling of computer performance Modern chips can have cores as high as today How do we keep all these cores busy? What about the coupling of different instruction streams based on dependencies in the computations? How do our software compilers detect these and reorganize code to layout the instruction streams to maximize independent instruction streams? 15

17 Is scalar performance acceptable? What kinds of problems do you think are not met by scalar performance? Can you imagine specific areas in computer science, software, engineering, data science, computational science, cryptography that would require large computation that might not be met by scalar performance increases? 16

18 How to meet the performance challenge? Today: Increases in the number of compute cores per chip Increases in the number of chips per computer Increases in the number of computers in a system Faster and large memory access Faster and large secondary storage arrays Faster and larger capacity networks Alternative compute architectures SIMD, MIMD, Clusters Alternative algorithms for large scale computation map/reduce, data federation, associative memory modeling, etc. 17

19 What about this? remember me? 18

20 Multicore processing What are the cores in a multicore system? The are all the same set of chip components, replicated and package. What if they weren t the same? What if different cores could perform different tasks? Like have some CPU and some GPU cores? or have some CPU, some GPU, some DSP, some FPGA, some Communications What might that look like and can these be made? How would that be programed? 19

21 Heterogeneous computing Such heterogeneous multicore chips can be made today. Problem is: We don t know how to program them in the general case. We can write software to solve specific problems on them, and when we do, they outperform most other software architectures So, how do we start to make sense of how to program these systems? I introduce Hydra.cs.mtech.edu 20

22 Hydra Heterogeneous Computing Platform CPU AMD Phenom II x T 6 compute cores Memory: 16GB of DDR3 1Ghz RAM GPUs: 3 x Nvidia Quadro FX GFLOPS (32-bit) (3/5/2007) 2 x Radeon HD GFLOPS (64-bit) (12/14/2010) 21

23 Call to Action!!!! Help solve the challenges associated with Heterogeneous Computing Program Language Translation Granularity Loosely Coupled (Hydra); Tightly Coupled Systems Mapping Problems Algorithm Development on these systems Operating System Run-time management of system components Architecture new architectures to support H.Computing Thank you and I can answer any questions 22