The GPU Accelerated Optimisation of the Water Management Systems

Tato práce se zabývá optimalizací řízení zásobní funkce vodohospodářských soustav. Vycházíme z dizertační práce Ing. Pavla Menšíka Ph.D., Automatizace řešení zásobní funkce vodohospodářské soustavy. Jako optimalizační metoda byla zvolena diferenciální evoluce. Daná metoda bude implmentována sekvečne a poté paralelizována nejdříve na procesoru a poté na GPUSubject of this thesis is optimalization of storage function of water management system. The work is based on dissertation thesis of Ing. Pavel Menšík Ph.D. Automatization of   storage function of water management system. As optimalization method was chosen diferential evolution. Sequential version of the method will be implemented as a first step, followed by CPU accelerated and   GPU accelerated versions.

Out-of-Core GPU Path Tracing on Large Instanced Scenes via Geometry Streaming

We present a technique for out-of-core GPU path tracing of arbitrarily large scenes that is compatible with hardware-accelerated ray-tracing. Our technique improves upon previous works by subdividing the scene spatially into streamable chunks that are loaded using a priority system that maximizes ray throughput and minimizes GPU memory usage. This allows for arbitrarily large scaling of scene complexity. Our system required under 19 minutes to render a solid color version of Disney\u27s Moana Island scene (39.3 million instances, 261.1 million unique quads, and 82.4 billion instanced quads at a resolution of 1024x429 and 1024spp on an RTX 5000 (24GB memory total, 22GB used, 13GB geometry cache, with the remainder for temporary buffers and storage) (Wald et al.). As a scalability test, our system rendered 26 Moana Island scenes without multi-level instancing (1.02 billion instances, 2.14 trillion instanced quads, ~230GB if all resident) in under 1h:28m. Compared to state-of-the-art hardware-accelerated renders of the Moana Island scene, our system can render larger scenes on a single GPU. Our system is faster than the previous out-of-core approach and is able to render larger scenes than previous in-core approaches given the same memory constraints (Hellmuth, Zellman et al, Wald)

GPUs as Storage System Accelerators

Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201

Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation

Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24–31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method