A Similarity Measure for GPU Kernel Subgraph Matching

Accelerator architectures specialize in executing SIMD (single instruction, multiple data) in lockstep. Because the majority of CUDA applications are parallelized loops, control flow information can provide an in-depth characterization of a kernel. CUDAflow is a tool that statically separates CUDA binaries into basic block regions and dynamically measures instruction and basic block frequencies. CUDAflow captures this information in a control flow graph (CFG) and performs subgraph matching across various kernel's CFGs to gain insights to an application's resource requirements, based on the shape and traversal of the graph, instruction operations executed and registers allocated, among other information. The utility of CUDAflow is demonstrated with SHOC and Rodinia application case studies on a variety of GPU architectures, revealing novel thread divergence characteristics that facilitates end users, autotuners and compilers in generating high performing code

Isopropylation of naphthalene over large pore zeolites

469-472Isopropylation of naphthalene with isopropanol (IPA) proceeds at 200°C over the large pore zeolites HY, Hβ, H-Mordenite and REY in the presence of N2. Over Hβ, conversion is more in N2 than when cyclohexane is used as solvent or in absence of N2. Conversions and selectivities to diisopropyl naphthalenes fall in the sequence Hβ > REY > HM > HY, but 2,6: 2,7 ratio is greatest with HM.With Hβ, conversion is maximum at 200°C, and increasing the IPA : naphthalene ratio leads to deactivation of the catalyst

Alkylation of naphthalene with t-butanol: use of carbon dioxide as solvent

Organic solvent free alkylation of naphthalene and t-butyl alcohol (TBA) over rare earth exchanged Y zeolite (REY) has been carried out in the presence of carbon dioxide at different process conditions (temperature between 418 and 458 K, pressure between 6 and 92 bar, TBA/naphthalene mole ratios of 1:4, and different periods of time (1.5-9 h)). The results suggest that fluid carbon dioxide can replace organic solvents as a medium for butylation of naphthalene over REY without loss of its shape selective characteristics

Model-based Iterative CT Image Reconstruction on GPUs

Computed Tomography (CT) Image Reconstruction is an important technique used in a variety of domains, including medical imaging, electron microscopy, non-destructive testing and transportation security. Model-based Iterative Reconstruction (MBIR) using Iterative Coordinate Descent (ICD) is a CT algorithm that produces state-of-the-art results in terms of image quality. However, MBIR is highly computationally intensive and challenging to parallelize, and has traditionally been viewed as impractical in applications where reconstruction time is critical. We present the first GPU-based algorithm for ICD-based MBIR. The algorithm leverages the recently-proposed concept of SuperVoxels, and efficiently exploits the three levels of parallelism available in MBIR to better utilize the GPU hardware resources. We also explore data layout transformations to obtain more coalesced accesses and several GPU-specific optimizations for MBIR that boost performance. Across a suite of 3200 test cases, our GPU implementation obtains a geometric mean speedup of 4.43X over a state-of-the-art multi-core implementation on a 16-core iso-power CPU.</jats:p