Collective Mind CMD demos

Below is a brief description of available CMD demos on compiler flag tuning and program dataset behavior exploration currently available in cM. Related scripts can be found in the $CM_ROOT/scripts/2-examples-of-auto-tuning. We currently support various versions of GCC, LLVM, ICC, Open64 and related libraries for Linux, Windows and Android that can easily co-exist with each other in cM repositories, and can be collaboratively extended.

Collective Mind web-based demos

In 2013, we deprecated our previous cTuning1 public repository (opened in 2008), and opened a new live Collective Mind repository at c-mind.org/repo. Any user can self-register and access the latest public codelets, benchmarks, datasets, packages, models, experimental data shared by the community. Users can also rank existing data or upload their own data.

Here we collect some demos at the live c-mind.org server to give users some ideas about what cM can do or help with (note that user can reproduce these actions locally using cM as described in detail in cM user guide.

Platforms, approximate power consumption and costs of platforms (2013/May) used for demos (watts for laptop were measured with the off-the-shelf power meter, cM running susan corners benchmark in a loop, and with max frequency):

	Approximate power consumption (max)	Approximate cost	Description
P1=Samsung Galaxy Y	?	~100 euros	Broadcom BCM21553 ARM11 processor (ARMv6), 0.832 GHz, Memory 290MB
P2=Archos 101IT	~7 Watts	~140 euros	ARM Cortex A8 single core 1GHz, L1=32KB, L2=512KB
P3=Dell Latitude D630	~46Watts	~180 euros	Intel Core2 Centrino T7500 2.2GHz, Merom, L1=32KB 8-way set-associative, L2=4MB, 16-way set associative
P4=Dell Latitude E4300	~50 Watts	~200 euros	Intel Core2 Duo Centrino P9400 2.4GHz, Penryn, L1=32KB, 8-way set associative, L2=6Mb, 24-way set associative, Memory=DDR3 4Gb, Dual 530MHz
P5=Dell Latitude E6320	~52 Watts	~800 euros	Intel Core i5 2540M 2.6GHz, Sandy Bridge, L1=32KB 8-way set associative, L2=256KB 8-way set associative, L3=3MB, 12-way set associative, Memory DDR3 dual channels, 8GB, 665MHz

Program multi-objective auto-tuning with Paretto-like filtering (compiler optimizations)

• Graph: Analysis of execution time variation (susan corners benchmark, Intel i5 processor, high-performance power scheme, 30 repetitions)
• Graph: Analysis of execution time variation (susan corners benchmark, Intel i5 processor, power scheme changed from max to min performance, 30 repetitions)

• Graph: compiler tuning (susan corners benchmark, Samsung Galaxy Y mobile, ARM v6, Sourcery GCC 4.7.2, 100 exploration points with random flags, kernel execution time vs binary size(
• Graph: compiler tuning (susan corners benchmark, Samsung Galaxy Y mobile, ARM v6, Sourcery GCC 4.7.2, 100 exploration points with random flags, kernel execution time vs binary size) - multigraph with references optimizations separated (-O1,-O2,-O3,-Os, -fast,etc)
• Graph: compiler tuning (susan corners benchmark, Samsung Galaxy Y mobile, ARM v6, Sourcery GCC 4.7.2, Pareto fronteer after 100 exploration points with random flags, kernel execution time vs binary size) - multigraph with references optimizations separated (-O1,-O2,-O3,-Os, -fast,etc)

• Graph: compiler tuning (susan corners benchmark, {Samsung Galaxy Y mobile, ARM v6 vs Archos 101 Internet Tablet, ARM v7}, Sourcery GCC 4.7.2, Pareto frontier after 100 exploration points with random flags, kernel execution time vs binary size) - multigraph with references optimizations separated (-O1,-O2,-O3,-Os, -fast,etc)
• Graph: compiler tuning (susan corners benchmark, Samsung Galaxy Y mobile, {Sourcery GCC 4.7.2 vs 4.6.3}, Pareto frontier after 100 exploration points with random flags, kernel execution time vs binary size) - multigraph with references optimizations separated (-O1,-O2,-O3,-Os, -fast,etc)
• Graph: compiler tuning (susan corners benchmark, Archos 101 Internet Tablet, {Sourcery GCC 4.7.2 vs LLVM 3.1}, 100 exploration points with random flags, kernel execution time vs binary size) - multigraph

Note, that above graphs can be easily converted to pdf, eps or png for publications or presentations - just press the associated button (when using Python MatplotLib as Graph engine).

Program crowd-tuning using available Android mobiles or cloud services (compiler optimizations)

As the first public crowd-tuning scenario of computer systems, any self-registered user can participate in systematizing of compiler flag tuning for multiple objectives on their own laptops, desktops and cloud/GRID services using standard cM framework, or using "Collective Mind Node" client on any mobile phone or Tablet with Android >= 2.x. Latest tuning results can be viewed here - currently we validate our past research and use data mining and machine learning to analyse this data and correlate most profitable compiler flags with program and architecture features. This is an on-going work and collaborations are welcome (we plan to continue collaborative development of such scenarios).

• Android application: Collective Mind Node to crowd source auto-tuning (current example - compiler flags tuning)
• Table: most profitable compiler flags found by the community (from mobiles, cloud services, etc)

Universal program/architecture parameter exploration for modeling and adaptation

We can easily perform tuning of various dimensions in our experimental pipeline. For example, we analyze CPI vs dataset size (or any other dimension in the pipeline that is very useful for data mining) for the ludcmp numerical codelet on several Intel architectures using Intel vTune amplifier (the same can be done with perf):

• Graph: P5
• {Graph: P3 vs P5}
• {Graph: P3 vs P4} - note alignment misses that relates to cache hierarchy

Note that these graphs motivate our run-time adaptation solution for heterogeneous architectures (servers, supercomputers, clouds) when depending on the code and dataset parameters, it's faster or more power efficient to run them on different processors or with different frequency. It is based on static multi-versioning and dynamic run-time adaptation schemes as described in ^{FCOP2005 LCWP2009 JVGP2009 FT2010}, and we plan to add these support for adaptive scheduling of CPU/CUDA/OpenCL kernels using cM in the future (see out future possible collaborative projects):

Optimization prediction using collective knowledge about program/architecture properties

• Interactive demo of online optimization predictor based on semantic program features (properties) extracted using MILEPOST GCC and combined with nearest-neighbor classifier to match continuously optimized codelets and programs by the crowd

Ctuning-cc/ctuning-fortran/ctuning-cpp demos (cM universal tuning and learning compiler wrapper) to predict compiler optimizations on the fly are available in cM and described in cM user guide.

If you would like to add (or see) more demos, please get in touch!