Aladdin is a pre-RTL, power-performance simulator for fixed-function accelerators.

Please read the licence distributed with this release in the same directory as this file.

If you use Aladdin in your research, please cite:

Aladdin: A Pre-RTL, Power-Performance Accelerator Simulator Enabling Large Design Space Exploration of Customized Architectures, Yakun Sophia Shao, Brandon Reagen, Gu-Yeon Wei and David Brooks, International Symposium on Computer Architecture, June, 2014

1. Boost Graph Library 1.55.0

   The Boost Graph Library is a header-only library and does not need to be built most of the time. But we do need a function that requires building libboost_graph and libboost_regex. To build the two libraries:

   1. Download the Boost library from here:

      wget http://sourceforge.net/projects/boost/files/boost/1.55.0/boost_1_55_0.tar.gz

   2. Unzip the tarball and set your $BOOST_ROOT:

      tar -xzvf boost_1_55_0.tar.gz
      cd boost_1_55_0
      export BOOST_ROOT=/your/path/to/boost_1_55_0/
      

   3. Run:

      ./bootstrap.sh

   4. Install:

      mkdir build
      ./b2 --build-dir=./build --with-graph --with-regex
      

   5. It will compile these two libraries to $BOOST_ROOT/stage/lib

2. Recent version of GCC including some C++11 features (GCC 4.5+ should be ok). We use GCC 4.8.1.

3. LLVM 3.4.0 and Clang 3.4.0 64-bit

4. LLVM IR Trace Profiler (LLVM-Tracer) LLVM-Tracer is an LLVM compiler pass that instruments code in LLVM machine-independent IR. It prints out a dynamic trace of your program, which can then be taken as an input for Aladdin.

   You can find LLVM-Tracer here:

   [https://github.com/ysshao/LLVM-Tracer.git]

   To build LLVM-Tracer:

   1. Set LLVM_HOME to where you installed LLVM

      export LLVM_HOME=/your/path/to/llvm
      export PATH=$LLVM_HOME/bin/:$PATH
      export LD_LIBRARY_PATH=$LLVM_HOME/lib/:$LD_LIBRARY_PATH
      

   2. Go to where you put LLVM-Tracer source code

      cd /path/to/LLVM-Tracer
      cd /path/to/LLVM-Tracer/full-trace
      make
      cd /path/to/LLVM-Tracer/profile-func
      make
      

1. Set $ALDDIN_HOME to where put Aladdin source code.

   export ALADDIN_HOME=/your/path/to/aladdin

2. Set $BOOST_ROOT to where put Boost source code and update $LD_LIBRARY_PATH

   export BOOST_ROOT=/your/path/to/boost
   export LD_LIBRARY_PATH=$BOOST_ROOT/stage/lib:$LD_LIBRARY_PATH
   

3. Build aladdin

   cd $ALADDIN_HOME
   make -j4
   

After you build Aladdin and LLVM-Tracer, you can use example SHOC programs in the SHOC directory to test Aladdin.

This distribution of Aladdin models fixed-function accelerators with scratchpad memory. Parameters like loop unrolling factors for each loop and memory partition (or memory bandwith) for each array allocated in your program. The functional units power models are based on OpenPDK 45nm and SRAM model from CACTI 5.3.

We prepare a script to automatically run all the steps above: generating trace, config file and run Aladdin here:

$ALADDIN_HOME/SHOC/scripts/run_aladdin.py

You can run:

cd $ALADDIN_HOME/SHOC/scripts
python run_aladdin.py triad 2 2 1 6

It will start Aladdin run at $ALADDIN_HOME/SHOC/triad/sim/p2_u2_P1_6ns

The last three parameters are unrolling and partition factors and enabling loop pipelining in the config files. You can sweep these parameters easily with Aladdin.

1. Go to $ALADDIN_HOME/SHOC/triad

2. Run LLVM-Tracer to generate a dynamic LLVM IR trace

   1. Both Aladdin and LLVM-Tracer track regions of interest inside a program. In the triad example, we want to analyze the triad kernel instead of the setup and initialization work done in main. To tell LLVM-Tracer the functions we are interested, set enviroment variable WORKLOAD to be the function names (if you have multiple functions interested, separated by comma):

   export WORKLOAD=triad
   export WORKLOAD=md,md_kernel
   

   2. Generate LLVM IR:

   clang -g -O1 -S -fno-slp-vectorize -fno-vectorize -fno-unroll-loops -fno-inline -emit-llvm -o triad.llvm triad.c

   3. Run LLVM-Tracer pass: Before you run, make sure you already built LLVM-Tracer. Set $TRACER_HOME to where you put LLVM-Tracer code.

   export TRACER_HOME=/your/path/to/LLVM-Tracer
   opt -S -load=$TRACER_HOME/full-trace/full_trace.so -fulltrace triad.llvm -o triad-opt.llvm
   llvm-link -o full.llvm triad-opt.llvm $TRACER_HOME/profile-func/tracer_logger.llvm
   

   4. Generate machine code:

   llc -filetype=asm -o full.s full.llvm
   gcc -fno-inline -o triad-instrumented full.s -lm -lz
   

   5. Run binary:

   ./triad-instrumented

   It will generate a file called dynamic_trace under current directory. We provide a python script to run the above steps automatically for SHOC.

   cd $ALADDIN_HOME/SHOC/scripts/
   python llvm_compile.py $ALADDIN_HOME/SHOC/triad triad
   

3.config file

Aladdin takes user defined parameters to model corresponding accelerator designs. We prepare an example of such config file at

cd $ALADDIN_HOME/SHOC/triad/example
cat config_example

partition,cyclic,a,8192,4,2  //cyclic partition array a, size 8192B, wordsize is 4B, with partition factor 2
partition,cyclic,b,8192,4,2  //cyclic partition array b, size 8192B, wordsize is 4B, with partition factor 2
partition,cyclic,c,8192,4,2  //cyclic partition array c, size 8192B, wordsize is 4B, with partition factor 2
unrolling,triad,5,2        //unroll loop in triad, define at line 5 in triad.c, with unrolling factor 2
pipelining,1               //enable loop pipelining, applied to all loops
cycle_time,6               //clock period, currently we support 1, 2, 3, 4, 5, and 6ns.

The format of config file is:

partition,cyclic,array_name,array_size_in_bytes,wordsize,partition_factor
unrolling,function_name,loop_increment_happened_at_line,unrolling_factor

Two more configs:

partition,complete,array_name,array_size_in_bytes //convert the array into register
flatten,function_name,loop_increment_happend_at_line  //flatten the loop

Note that you need to explicitly config how to partition each array in your source code. If you do not want to partition the array, declare it as partition_factor 1 in your config file, like:

partition,cyclic,your-array,size-of-the-array,wordsize-of-each-element,1

4. Run Aladdin Aladdin takes three parameters: a. benchmark name b. path to the dynamic trace generated by LLVM-Tracer c. config file Now you are ready to run Aladdin by:

   cd $ALADDIN_HOME/SHOC/triad/example
   $ALADDIN_HOME/common/aladdin triad $ALADDIN_HOME/SHOC/triad/dynamic_trace config_example
   

   Aladdin will print out the different stages of optimizations and scheduling as it runs. In the end, Aladdin prints out the performance, power and area estimates for this design, which is also saved at <bench_name>_summary (triad_summary) in this case.

   Aladdin will generate some files during its execution. One file you might be interested is <bench_name>_stats which profiles the dynamic activities as accelerator is running. Its format:

   line1: cycles,<cycle count>,<# of nodes in the trace>
   line2: <cycle count>,<function-name-mul>,<function-name-add>,<each-partitioned-array>,....
   line3: <cycle 0>,<# of mul happend from functiona-name at cycle 0>,..
   line4: ...
   

   A corresponding dynamic power trace is <bench_name>_stats_power

5. Design Space We provide a simple script to show how to run Aladdin to generate the design space of a workload. The script is

$ALADDIN_HOME/SHOC/scripts/gen_space.py

You can run:

cd $ALADDIN_HOME/SHOC/scripts
python gen_space.py triad

You can replace triad with any SHOC benchmark that we provide. It will automatically generate the config file and trigger Aladdin.

It sweeps unrolling and partition factors, as well as turn on and off loop pipelining, to generate a power-performance-area design space. At the end of the script, if you have matplotlib installed, it will generate 6 plots of design spaces in terms of total-power vs cycles, fu-power vs cycles, mem-power vs cycles, total-area vs cycles, fu-area vs cycles, and mem-area vs cycles.

1. This distribution of Aladdin models the datapath and local scratcpad memory of accelerators but not includes the rest of the memory hierarchy. We are working on providing a release of Aladdin with a memory hierarchy. At the same time, if you are interested in doing your customized integration, we will provide a user`s guide on how to do the integration.

2. No Function Pipelining:

This distribution of Aladdin does not model function pipelining. In this case, if a program has multiple functions built into accelerators, This distribution of Aladdion assumes only function executes at a time.

3. Power Model Library:

This distribution of Aladdin characterizes power using OpenPDK 45nm technology. The characterized power for functional units are in utils/power_delay.h. If you are interested in trying different technologies, modify the constants there with your power delay characteristics and then recompile Aladdin. We will be releasing the microbenchmark set that we used to do power characterization soon.

============================================ Sophia Shao,

shao@eecs.harvard.edu

Harvard University, 2014