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Advancing GPGPU: Harnessing GPUs for General-Purpose Computing

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This paper explores the evolution of Graphics Processing Units (GPUs) as powerful, flexible tools for general-purpose computation (GPGPU). Modern GPUs exhibit remarkable programmability and precision, supporting high-level languages and advanced features like 64-bit floating point computations. The paper introduces stream programming abstraction, enabling efficient parallel processing through data streams and kernels. By analyzing practical applications, including a simulation of cloth dynamics, it highlights the efficiency of GPGPU in solving complex computational problems. The findings point towards new potential applications of these techniques in broader contexts.

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Advancing GPGPU: Harnessing GPUs for General-Purpose Computing

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  1. David Angulo Rubio FAMU CIS GradStudent GPGPUGeneral Purpose Programmability on Graphic Processors Units

  2. Introduction • GPU(Graphics Processing Unit) on video cards has evolved during the last years. They have become extremely powerful and flexible • Programmability • Precision • Power • GPGPU computing is an emerging field which objective is to harness GPUs for general-purpose computation

  3. GPU Performance Trends

  4. Motivation: Flexible and Precise • Modern GPUs are deeply programmable • Programmable pixel, vertex, video engines. • Solidifying high level language support • Modern GPUs support high precision • 32 bit floating point throughout the pipeline • High enough for many (not all) applications • Newest GPUs have 64bit support

  5. Stream Programming Abstraction • Streams • Collection of data records • All data is expressed in streams • Kernels • Inputs/outputs are streams • Perform computation on streams • Can be chained together stream KERNEL stream

  6. Stream Programming Abstraction Dolphin Triangle Mesh

  7. Stream Programming Abstraction • Benchmark Funnel: In this simulation, a cloth falls into a funnel and pass through it under the pressure of a ball. This model has 47K vertices, 92K triangles, and a lot of self-collisions. Our novel GPU-based CCD algorithm takes 4.4ms and 10ms per frame to compute all the collisions on a NVIDIA GeForce GTX 480 and a NVIDIA GeForce GTX 285, respectively.

  8. Stream Programming Abstraction

  9. Why Streams • Ample computation by exposing parallelism • Streams expose data parallelism • Multiple streams elements can be processed in parallel • Pipeline (task) parallelism • Multiple tasks can be processed in parallel • Kernels yield high arithmetic intensity • Efficient communication • Producer consumer locality • Predictable memory access pattern • Optimize for throughput of all elements, not latency of one • Processing elements at once allows latency hiding

  10. CPU GPU ANALOGIES Stream/Data array = Texture Memory read= Texture Sample

  11. Structuring a GPU Program • Cpu assembles input data • Cpu transfers data to GPU(GPU “main memory” or “device memory”) • Cpu calls GPU program (or set of kernels). GPU runs out of GPU main memory. • When GPU finishes, CPU copies back results into CPU memory. • Recent interfaces allow overlap • What lessons can we draw from this sequence of operations

  12. Kernels ADVECT CPU GPU KERNEL / LOOP BODY / ALGORITHM STEP = Fragment Program You write one program. It runs on every vertex/fragment.

  13. Conclusion • Can we apply these techniques to more general problems? • GPUs should excel at tasks that : • Require ample computation • Regular computation • Efficient communication

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