An Irregular Approach to Large-Scale Computed Tomography on Multiple Graphics Processors Improves Voxel Processing Throughput

Jimenez, Edward S.; Orr, Laurel Jeffers; Thompson, Kyle R.

doi:10.1109/sc.companion.2012.42

Cited by 9 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm described in Ref. [13] is one such GPU-based CT reconstruction which goes beyond the direct port of a CPU-based algorithm to GPU kernels and improves performance even more by taking advantage of the GPU-specific capabilities and hardware.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, as first described in Ref. [13], GPU textures are fully exploited by using small sets of X-ray images to be prefetched from host memory to device memory. The inner loop finishes once the sub-volume has been successfully written to storage by the CPU threads, and the outer loop finishes once the entire volume has been reconstructed (see Algorithm 1 for a high-level description).…”

Section: Methodsmentioning

confidence: 99%

“…[13] suffered from prolonged GPU idle time when performing storage operations. The bottleneck was a consequence of stalling GPU computation while writing the reconstructed image planes to storage media.…”

Section: Approachmentioning

confidence: 99%

“…[13]. The fast texture cache hit rate of the GPUs is exploited by dynamically fitting the number of X-ray images to transfer to the GPU to a user inputed slice-to-texture ratio.…”

Section: Gpu Texturesmentioning

confidence: 99%

“…It will then use the large-scale (1 Teravoxel), synthetic dataset from Ref. [13] to evaluate the algorithm's performance and the GPU scalability in order to predict this method's applicability to current and future industrial datasets.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

High performance graphics processor based computed tomography reconstruction algorithms for nuclear and other large scale applications.

Jimenez

2013

View full text Add to dashboard Cite

The goal of this work is to develop a fast computed tomography (CT) reconstruction algorithm based on graphics processing units (GPU) that achieves significant improvement over traditional central processing unit (CPU) based implementations. The main challenge in developing a CT algorithm that is capable of handling very large datasets is parallelizing the algorithm in such a way that data transfer does not hinder performance of the reconstruction algorithm. General Purpose Graphics Processing (GPGPU) is a new technology that the Science and Technology (S&T) community is starting to adopt in many fields where CPU-based computing is the norm. GPGPU programming requires a new approach to algorithm development that utilizes massively multi-threaded environments. Multi-threaded algorithms in general are difficult to optimize since performance bottlenecks occur that are non-existent in single-threaded algorithms such as memory latencies. If an efficient GPU-based CT reconstruction algorithm can be developed; computational times could be improved by a factor of 20. Additionally, cost benefits will be realized as commodity graphics hardware could potentially replace expensive supercomputers and high-end workstations. This project will take advantage of the CUDA programming environment and attempt to parallelize the task in such a way that multiple slices of the reconstruction volume are computed simultaneously. This work will also take advantage of the GPU memory by utilizing asynchronous memory transfers, GPU texture memory, and (when possible) pinned host memory so that the memory transfer bottleneck inherent to GPGPU is amortized. Additionally, this work will take advantage of GPU-specific hardware (i.e. fast texture memory, pixel-pipelines, hardware interpolators, and varying memory hierarchy) that will allow for additional performance improvements.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

“…[13]. The fast texture cache hit rate of the GPUs is exploited by dynamically fitting the number of X-ray images to transfer to the GPU to a user inputed slice-to-texture ratio.…”

Section: Gpu Texturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

High performance graphics processor based computed tomography reconstruction algorithms for nuclear and other large scale applications.

Jimenez

2013

View full text Add to dashboard Cite

show abstract

A high-performance and energy-efficient CT reconstruction algorithm for multi-terabyte datasets

Jimenez

Orr

Thompson

et al. 2013

2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC)

View full text Add to dashboard Cite

Cluster-based approach to a multi-GPU CT reconstruction algorithm

Orr

Jimenez

Thompson

2014

2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

Self Cite

View full text Add to dashboard Cite

Conventional CPU-based algorithms for Computed Tomography reconstruction lack the computational efficiency necessary to process large, industrial datasets in a reasonable amount of time. Specifically, processing time for a single-pass, trillion volumetric pixel (voxel) reconstruction requires months to reconstruct using a high performance CPU-based workstation. An optimized, single workstation multi-GPU approach has shown performance increases by 2-3 orders-of-magnitude; however, reconstruction of future-size, trillion voxel datasets can still take an entire day to complete. This paper details an approach that further decreases runtime and allows for more diverse workstation environments by using a cluster of GPU-capable workstations. Due to the irregularity of the reconstruction tasks throughout the volume, using a cluster of multi-GPU nodes requires inventive topological structuring and data partitioning to avoid network bottlenecks and achieve optimal GPU utilization. This paper covers the cluster layout and non-linear weighting scheme used in this high-performance multi-GPU CT reconstruction algorithm and presents experimental results from reconstructing two large-scale datasets to evaluate this approach's performance and applicability to future-size datasets. Specifically, our approach yields up to a 20 percent improvement for largescale data.

show abstract

An Irregular Approach to Large-Scale Computed Tomography on Multiple Graphics Processors Improves Voxel Processing Throughput

Cited by 9 publications

References 11 publications

High performance graphics processor based computed tomography reconstruction algorithms for nuclear and other large scale applications.

High performance graphics processor based computed tomography reconstruction algorithms for nuclear and other large scale applications.

A high-performance and energy-efficient CT reconstruction algorithm for multi-terabyte datasets

Cluster-based approach to a multi-GPU CT reconstruction algorithm

Contact Info

Product

Resources

About