GPU-accelerated Evaluation Platform for High Fidelity Networking Modeling 11 December 2007 Alex Donkers Joost Schutte

Contents Summary of the paper EvaluationQuestions

Using commercial graphic cards to speed up execution of network simulation models. Network simulators high fidelity performance evaluation  more detailed models higher computation cost  speed up technique GPU = graphics processing unit Computational power GPU against CPU widening.

Computational power of GPU and CPU (courtesy of Ian Buck, Standford Univ.)

GPU superior because: Stream processing model Spatial parallelism Necessities for GPU usage: Identification data parallelism in network simultions Software abstraction Goal: Design evaluation platform architecture Efficient utilisation of computational processors of GPUs and CPU, memory, IO and other recources. Available in commodity desktops.

Commodity desktop equipped with multiple GPUs With Vidia SLI technology more GPUs in singel system.

Suitability for different types of computation: CPU = high performance on single thread of execution GPU = many more arithmetic units extremely high data parallel and instruction parallel execution Evaluating process high-fidelity network modeling involves: task-parallel computation  multi CPU data-parallel computation  GPUs Features necessary for GPU acceleration: highly data parallel arithmetic-intensive

Power of GPUs is showed by implementing two cases from a network environment in both CPU and GPU. Compared are speed and acurracy of the simulation results. Two cases: Fluid-flow-based TCP model = predicts the traffic dynamics at active queue management routers. Adaptive antenna model =calculates weight of the beam former in direction minimizing mean squared error.

Fluid-flow-based TCP model TCP flows and active queue management Routers are modelled with Stochastic differential equationsTCP flows and active queue management Routers are modelled with Stochastic differential equations Transform Stochastic differential equations into ordinary differential equations (ODEs) for CPU useTransform Stochastic differential equations into ordinary differential equations (ODEs) for CPU use CPU-based implementation uses a ODE solver, ODE45, provided in MatlabCPU-based implementation uses a ODE solver, ODE45, provided in Matlab GPU maps all data structures in CPU to on-board memory in GPUGPU maps all data structures in CPU to on-board memory in GPU

Fluid-flow-based TCP model Time varying state of routers require recomputation of ODE solvers periodicallyTime varying state of routers require recomputation of ODE solvers periodically Execution speed of model is significantly affected by execution speed of ODE solversExecution speed of model is significantly affected by execution speed of ODE solvers Implementing ODE solver in GPU can significantly increase size of network that can be evaluatedImplementing ODE solver in GPU can significantly increase size of network that can be evaluated

Adaptive antenna model recursively updates weights of the beamformers in the direction minimizing mean squared error (MSE) recursively updates weights of the beamformers in the direction minimizing mean squared error (MSE) Recursive least squares (RLS) algorithm is usedRecursive least squares (RLS) algorithm is used Implement data layout and operations of arrays of complex numbers in GPUImplement data layout and operations of arrays of complex numbers in GPU

Evaluation Strong points Weak points Simulation models Conclusion & Future work

Strong Points Highly data-parallel Arithmetic-intensive

Weak Points Processes constitute largely sequential operations Processes require bit-wise operations Solution: Use DSP platform Real-time simulation

Evaluation simulation models Hardware Platform: Dell Dimension desktop Intel (dual core) 3GHz Pentium 4 CPUIntel (dual core) 3GHz Pentium 4 CPU 1GB DDR2 memory nVidia GeForce 7900GTXnVidia GeForce 7900GTX 512MB texture memory Vertex & fragment program: programmed with openGL and GLSL programmed with openGL and GLSL

Simulation models  Differences between GPU & CPU based simulation for Fluid-flow-based TCP model Difference in prediction of traffic dynamicsDifference in prediction of traffic dynamics Difference in execution timeDifference in execution time GPU outperforms CPU for with 256 flows & 256 queues or more because of larger number of iterations in GPU based ODE solver GPU outperforms CPU for with 256 flows & 256 queues or more because of larger number of iterations in GPU based ODE solver

Normalized ODE solver Evaluation Time

Simulation models Adaptive antenna model GPU-based simulation runs faster than CPU- based one when antenna array size exceeds 256GPU-based simulation runs faster than CPU- based one when antenna array size exceeds 256 Execution time of GPU-based implementation linear decreases with respect to the number of sub-carriers due to parallel processingExecution time of GPU-based implementation linear decreases with respect to the number of sub-carriers due to parallel processing

Simulation Execution Times

Conclusions & Future work GPU’s can achieve a speedup of 10x without loss of accuracy High fidelity network simulations can be accelerated by parallel use of CPU & GPU units Integrate GPU-implemented modules into existing simulation-based network evaluation platform

Questions?