Gpgpu accelerated simulation and parameter tuning for neuromorphic applications jeff krichmar, kris carlson, michael beyeler, nikil dutt cognitive anteater robotics lab carl department of cognitive sciences department of computer science university of california, irvine, usa march 4, 2014 gpu accelerated simulation and parameter. An interactive physicallybased method which simulates a piece of cloth colliding with a sphere. Fast acceleration of 2d wave propagation simulations using. Acceleration of cardiac tissue simulation with graphic processing units. Using the gpu to supplement the cpu during flow simulation. Science of sw simulators, acceleration, protyping, emulation. Gpubased system objects look and behave much like the other system objects in the communications toolbox product. We demonstrate our gpgpu simulator on a target architecture composed by several cores i. Accelerating gpgpu architecture simulation nilanjan goswami, ph. With rendering process running on both gpu and cpu simultaneously, render speed is much faster than conventional renderer that only exploits cpus computing power see the benchmark chart below. Fast acceleration of 2d wave propagation simulations using modern. Krichmar 1,2 abstract neuromorphic engineering takes inspiration from biology to design brainlike systems that are extremely lowpower, faulttolerant, and capable of adaptation to complex environments. And it comes from ieee transactions on parallel and distributed systems, may 2015.
Nilanjan goswami gpu architect advanced computing lab. Gpu acceleration of molecular modeling applications. Graphics processing units gpu, due to their massive computational power with up to thousands of concurrent threads and generalpurpose gpu gpgpu programming models such as cuda and opencl, have opened up new opportunities for speeding up generalpurpose parallel applications. The simulation of manycore architectures exhibits indeed a high level of parallelism and is inherently parallelizable, but gpgpu acceleration of architectural simulation requires an indepth revision of the data structures and functional partitioning traditionally used in parallel simulation. The soft gpgpu is based on the g80 architecture 3the first dedicated generalpurpose gpu from nvidia with compute capability 1. Paul richmond, a vice chancellors research fellow at the university of sheffield a cuda research center. He works in the area of 3d graphics pipeline architecture optimization for. Simulation acceleration using gpus turbo, ldpc, viterbi, convolutional coding communications toolbox includes gpubased system objects that can target execution of the algorithm on a graphical processing unit gpu rather than on a cpu. Efficiently accelerating an algorithm using a gpu requires. Arm isa based, with instruction and data caches, connected through a networkonchip noc. The important difference is that the algorithm is executed on a. However, the simulation of complex systems within a reasonable amount of time remains a computational challenge. Gpu is architected for hpc modeling and simulation workloads and ai training.
Mobile gpgpu acceleration of embodied robot simulation simon jones1,4,matthewstudley2,andalanwin. Accelerating gpgpu microarchitecture simulation ugentelis. Some of the tradeoffs among the various execution platforms that have been discussed so far are shown in figure 3. The simulation is implemented using three popular technologies for generic programming on the gpu.
Gpgpu simulation acceleration of the communicated agent state is relatively small 32 bytes. The first kernel which doesnt take advantage of shared memory is 15% faster than the second kernel which uses shared memory. This paper presents a general purpose graphic processing unit gpgpu accelerated sparse linear solver for fast simulation of onchip coupled problems using nvidia and ati gpgpu accelerators on a multicore computational cluster and evaluates parallelization strategies from a computational perspective. Toward gpgpu accelerated human electromechanical cardiac simulations. These include sensitivity to cache size and associativity, barrel and switchonstall multithreaded instruction scheduling, and software vs. This can require thousands of forward light propagation simulations to. As architecture and software complexity increases, simulator performance be. A synthesizable gpu architectural model for general. Were upgrading the acm dl, and would like your input. For example, physx still seems to cripple cpu performance on purpose. The twentyfold acceleration provided by the gpu decreases the runtime for the nonbonded force evaluations such that it can be overlapped with bonded forces and pme longrange force calculations on the cpu.
However, the calculation of a function on a lattice where all points are independent is an ideal application for gpu acceleration, and as recently reported in the journal of computational chemistry, the use of gpus to accelerate coulombbased ion placement leads to speedups of 100 times or more, allowing large structures to be properly ionized in less than an hour on a single desktop computer. To demonstrate nyamis viability as a gpu research platform, we exploit its. Testing and validation of a prototype gpgpu design for fpgas. The cuda model is a hardware and software architecture to perform computations on the gpu as a data. From what i have observed, havok does a significantly better job for rigid simulation than physx, especially their new havok physics 20. Simulation acceleration using gpus gpubased system objects. Parallel simulation based on gpuacceleration springerlink. These and other cpubound operations must be ported to the gpu before further acceleration of the entire namd application can be realized. Toward gpgpu accelerated human electromechanical cardiac. It includes the broad physical modeling capabilities needed to model flow, turbulence, heat transfer, and reactions for industrial applications. Cuda and opencl applications typically contain 10s of thousands of threads making them an interesting workload for future many core architecture research.
Find out how your company can benefit from plm built on salesforce. Carlson1, michael beyeler2, nikil dutt2, jeffrey l. Towards prototyping and acceleration of java programs onto intel fpgas. Generalpurpose computing on graphics processing units gpgpu, rarely gpgp is the use of a graphics processing unit gpu, which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit cpu. It includes the broad physical modeling capabilities needed to. Gpgpusim is a detailed simulator that models a modern gpu running applications written in cuda and opencl. Gpu acceleration software software free download gpu acceleration software top 4 download offers free software downloads for windows, mac, ios and android computers and mobile devices. Gpgpusim provides a detailed simulation model of a contemporary gpu running cuda andor opencl workloads and now includes an integrated and validated energy model, gpuwattch. Abstract in this paper, opencl is used to target a general purpose graphics processing unit gpgpu for acceleration of 2 modules used in a synthetic aperture radar sar simulator. It also has a more modular software design that simpli. Exploring gpgpu acceleration of processoriented simulations.
Gpu performance bottlenecks department of electrical engineering es group 28 june 2012 2. Gpgpu accelerated krylov methods for compact modeling of. The paper i want to present today is gpu acceleration for simulating massively parallel manycore platforms. Gpuaccelerated ansys fluent get started today with this gpu ready apps guide. Barra, a parallel functional gpgpu simulator archive ouverte hal. For ansys and other programs that have been built to utilize gpu the workstation cards and the tesla card that they mention can be used to speed up the.
In the introduction, we are going to go through some basic concepts. This paper presents tbpoint, an infrastructure based on profilingbased. As the complexity of these games scale, so does the. Nilanjan is a gpu architect at the advanced computing laboratory, samsung san jose, ca. Simulation acceleration involves mapping the synthesizable portion of the design into a hardware platform specifically designed to increase performance by evaluating the hdl constructs in parallel. Summaryin this paper, we look at the acceleration of weakly coupled electromechanics using the graphics processing unit gpu. Im not very familiar with how state of the art physics engine works, but by testing alone i cannot get a very accurate test results. Gpgpu accelerated sparse linear solver for fast simulation. Opalrt simulation acceleration maximizes computational resources to significantly reduce the processing time currently required for computing model results. Architecture simulation for gpgpu kernels can take a significant amount of time, especially for largescale gpgpu kernels. The remaining portions of the simulation are not mapped into hardware, but run in a software simulator on a pcworkstation.
To the best of our knowledge, this is the first time that gpgpu on mobile devices have been used to accelerate robot simulation, and moves towards providing an autonomous robot with an embodied whatif capability. Gpgpuaccelerated parallel and fast simulation of thousand. Gpu optimized for hpcai acceleration based on the xe architecture. We assume the nvidia gpu as a more or less generic architectural template 4. Gpu produces a 43% reduction in fluent solution time on an intel xeon e52687 8 core, 64gb workstation equipped with an nvidia k40 gpu design impact increased simulation throughput allows meeting deliverytime requirements for engineering services. In what follows, a new software architecture is presented as basis for optimized multiphysics simulation on gpuaccelerated clusters and workstations. Gpgpu accelerated simulation and parameter tuning for.
Gpu acceleration software software free download gpu. Gpgpu, the leading role of the tutorial, stands for general purpose computing on graphics processing unit, which is a newly emerged technique for computational acceleration. A list of topics to be covered and some of their related bibliography 1. This paper presents work done in the incorporation of gpgpu technology to accelerate krylov based algorithms used for compact modeling of onchip passive integrated structures within the workflow of the chameleonrf project.
Our experimental results show that gpgpuminibench can speed up gpgpu architectural simulation by a factor of 49 on average and up to 589, with an average ipc error of 4. Monte carlo light propagation simulator written in software. Intel unveils new gpu architecture with highperformance computing and ai acceleration, and oneapi software stack with unified and scalable. Based on the example from nvidia gpu computing sdk i created two kernels for the nbody simulation. I took a quick look at that link that you attached and it looks like that is a program that already utilizes the gpu for the simulation. Unfortunately, presilicon architectural simulation of modernday gpgpu. By characterizing existing gpgpu workloads, we find that existing cpu and gpu architectural simulation acceleration solutions cannot be readily applied to gpgpu simulation. Gpgpu accelerated simulation and parameter tuning for neuromorphic applications kristofor d. Citeseerx 2011 11th ieeeacm international symposium on. Intel unveils new gpu architecture with highperformance.
Gpgpu acceleration using opencl for a spotlight sar simulator. Generalpurpose computing on graphics processing units. With xf em simulation software, there is no limit to the resources you can exploit to. Theas prestomc and prestoao support gpu render computing.
The fastest em simulation gpu acceleration is free in remcoms xfdtd. Ansys fluent is a software tool designed to run computational fluid dynamics cfd simulations. There are a couple of things that you might need to know before we take o. Most of its key features like multithreading, vector. Mobile gpgpu acceleration of embodied robot simulation. Simulation acceleration simulation solutions opalrt. For example, nvidias fermi architecture gpu card tesla c2050. Gpgpu cloth simulation using glsl, opencl and cuda. In this white paper, learn how you can configure freely without requiring any custom development, and quickly update configurations as your needs evolve. Gpu acceleration for simulating massively parallel many. Pauls research interests relate to the simulation of complex systems and to parallel computer hardware. Opalrt delivers the fastest performance for todays demanding simulation tasks, including electromagnetic transient emt simulation, for faster, more efficient simulation. With the appearance of cuda that carried out by nvidia, the gpu is used for generalpurpose computation is easier and cheaper, there are many high performance computation questions in simulation field, such as the simulation of the electromagnetic environment, the solution of higher order differential equations, the simulation data processing.
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