Parallel Processors Todd Charlton Eric Uriostique

Current Technology Hard to find a single core processor anymore. Cell phones, Labtops, etc. Large systems can contain up to 512+ processors

The Motivation Divide and Conquer – Higher Throughput Lower Power Consumption P = CV 2 f

The Motivation We need more performance on same power budget. How? Remember: P = CV 2 f Scale voltage and frequency to 80% P = C *.8 2 [V] *.8 [f] This drops power by 50% Add additional core Result = 1.6x Speedup with same power

The Motivation How about reducing power consumption but keeping the same performance? Remember: P = CV 2 f Scale voltage and frequency by 50% P = C *.5 2 [V] *.5 [f] This drops power to 12.5% Add additional core Result = 25% of original power consumption with same performance

Amdahl’s Law “Speed-up is limited by amount of work that can be done in parallel” Credit: watermint.org

Ways To Parallelize 1. Multi-Threading: Multi-thread your application on one chip More elegant 2. Multi-Processing: Flash serial code to separate chips No worrying about scheduling!

Let’s Multi-Thread One Application: Counting maize pixels 2 Processors 4 Processors

Multi-Threading in µProcessors Spin Propeller Processor Multi-Thread on 8 cores One application run on 8 cores Uses it’s own high level language and a form of Assembly In CMU Cam4

Problems with Multi-Threading Steep learning curve Learning the Language Parallel Slowdown Lot of time to set up a new thread. If that thread does not have much work, not worth the overhead

Multi-Threading Libraries Cannot program serially to take advantage of Parallel Processing Intel’s Thread Building Blocks (TBB) OpenMP Boost and pthread All of these are libraries in C/C++

Multi-Processing: Beaglebone Processor 720 MHz ARM Cortex-A8 3D graphics accelerator ARM Cortex-M3 for power management 2x Programmable Realtime Unit RISC CPUs PRUs share memory space with A8

Shared Memory Space

Multi-Processing: Custom with Message Passing Designate a processor for each frequent tasks Send messages to "Boss" as necessary Since every processor's workload is minimal, slower and low power chips can be used Overall = Same system performance

Message Passing

Problems with Multi-Processing Shared Memory Space Boards like this are hard to find and configure Message Passing Can’t assume messages are received immediately

Recap Go parallel if you want: Higher Throughput Lower Power Two Ways: Multi-Threading – Spin Speed up one Application Multi-Processing – Beaglebone Do more tasks at same time Don’t forget Amdahl’s Law!

Questions