post compiler software optimization for reducing energy

Post-compiler Software Optimization for Reducing Energy

Eric Schulte, Jonathan Dorn, et all

Presented By: Abhishek Abhyankar

MS Computer Science Virginia Tech

08-May-15 Computer Architecture CS 5504 Spring 2015 1

Traditional Way of doing things

• Make a case for reduction in energy consumption.

• Traditionally Energy optimization handled in Hardware.• Voltage Scaling , Heterogeneous Cores, Specialized Cores and, many others.

• On Software side, its mainly concerned about increasing speed and reducing size of the compiled code.• Extracting Instruction, Thread, and Data level parallelism.


Post Compile Software Optimization

• Handle the Optimizations on Software level.

• Take the compiled code output from standard compiler.

• How can this be achieved ? One of the approach is :

“Genetic Optimization algorithm which uses concepts from Evolutionary computation which stochastically mutilates the software for optimum implementation, all this while preserving strict functional semantics.”


Background Concepts

• Functional Vs Non Functional Requirements.• On going debate between

• Functional Requirements: Adhering to Specifications, Correctness of the code.

• Non Functional Requirements: Memory Utilization, Energy Consumption.

• Stochastic Methods• Used heavily in Evolutionary computation.

• Randomly trying out different combinations.


Background Concepts .. continued

• Profile Guided Optimizations.• Program is profiled by running it and gathering run time data.

• Call graph generation.

• Enforcing “nearest is the best” policy.

• Software robustness even after mutilation.• Random mutilations of the software preserve the semantic meaning.

• Many implementation possible which lead to same semantic goal.


Background Concepts .. continued

• Evolutionary Computation.• Darwinian principles.

• Generally applied in black box approach.

• Steady State Algorithms.• After each iterations candidates are simply inserted back in populous.

• Best among them is selected or rather worse is deleted.


Genetic Optimization Algorithm(GOA)

• “Genetic Optimization algorithm which uses concepts from Evolutionary computation which stochastically mutilates the software for optimum implementation, all this while preserving strict functional semantics.”

• Takes in three inputs to start.• Benchmark Applications or Kernels.

• Test Suites which validate the mutation.

• Fitness Function


High-level working of GOA


GOA Working .. continued

• Take the program

• create many random variants of the program by changing the order of the instructions , deleting and editing some

• Test the new variant with the test suites which are submitted

• If they pass then check for improvement in the non functional requirements function

• If yes spit out the assembly code as an optimized code after applying Minimization technique.


Representation of Assembly code

• Very simple strategy adopted to represent the assembly code.

• Each line will have a cell in an array.

• One line can be broken down and also have multiple cells too.

• The Augmented instructions are avoided.• Limits the search space.


Experimental Setup and Benchmark Kernels:

• Intel machine used as an example of Desktop computer.• I7 , 4 Cores, 8 GB Ram

• AMD machine used as an example for Server Scale machine.• 48 Cores, 128 GB Ram

• 8 Kernels from PARSEC benchmark suite used.• Blackscholes, bodytrack, ferret, fluidanimate, freqmine, swaptions, vips, and

x264

• They should at-least keep the underlying Architecture running for 1 sec and produce output.


Input Test Suites

• Comprehensive test suites for each kernel.

• Smallest input size of the test suite is considered.

• Just for validating requirements specification, stress or border testing not needed that this point.


Fitness Function

• GOA proposes a linear scalar energy model

• Hardware counters are captured using the “perf” utility in Linux.• Tightly coupled with the underlying Architecture and Fine grained.

• Heavily dependent on time factor.


power = Cconst + Cins + Cfpos + Ctca + Cmem

energy = seconds power

ins fpos tca mem

cycle cycle cycle cycle

Constants Derived from Empirical Study


Minimization Technique

• Iteration tend to create redundant patterns of code.

• The goal is to get the best energy efficiency with least amount of changes.

• Delta Debugging is used to compare and remove redundant , non influential changes.


Code Example of GOA


Post processing the optimized code

• Execute the original code with Held-out test suite.• Obtain Wall-Socket real measurements.

• Execute the optimized code with Held-out test suite.• Obtain Wall-Socket real measurements.

• Compare the two results and find out patterns which saw improvements and percentage improvement in Energy consumption.


Results

• In blackscholes kernel GOA caught the induced repeatition loop and found a way around it.

• In swaptions kernel GOA gave a 42% energy savings.• Have to take it with a pinch of salt though.

• In vips kernel , the cache misses actually increased instructions lines decreased and hence 20% improvement was observed.


Interesting Observations

• 7% average error found in most prediction models and so as in GOA.• But still works fine with it.

• Empirical studies show that GOA might be better suited to finding efficient sequence of assembly instructions but not efficient memory access patterns.

• Energy reduction percentage is consistently more on AMD machines.• But mainly due more opportunities due to bigger machine.


QoS dependent Optimization

• “Relaxed” preservation of semantics and more emphasis on QoS.

• The plug and play testing suite policy gives the developer option of making GOA strict or loose on semantics.

• Relaxed functional requirements provide much more energy efficiency but risk is taken by the developer to see the program semantic does not break.


Key contributions

• Genetic Optimization Algorithm (GOA) combines insights from profile-guided optimization, superoptimization, evolutionary computation and mutational robustness.

• This technique gave 20% average energy savings across all benchmarks.

• Very simple and mostly leverages from already available techniques.


Drawbacks of GOA

• Energy constant are taken empirically over repeated run on specific hardware.• Introducing GOA on new architecture will take considerable amount of work.

• Non deterministic approach makes it almost impossible to restore to earlier code path after the software is changed even slightly.• Must provide indexing of the code paths and remember them.

• Very High quality test suites “required”• Failure to provide them might result in over optimized false working code.


Proposed Future Work

• Currently only applied to x86.• A matrix implementation proposed as a solution to this problem.

• Indirect selection can optimize one parameter at the cost of worsening other.• Should be generalized to Java Byte code and ARM.

• Instead of Compiler which takes a predefined “agreed” path, a code should be compiled with multiple compiler using multiple paths and then best should be selected.


Questions / Discussion


post compiler software optimization for reducing energy

Engineering