Accelerating Kirchhoff Migration by CPU and GPU Cooperation

In this paper, we discuss the porting strategy and the achieved performance of Petrobras production code that implements Kirchhoff prestack time and depth seismic migration algorithms on a cluster of 64 GPUs and 256 CPU cores. The time migration of a real-world survey was 20 times faster in the full cluster with GPUs than in the full cluster without GPUs. The introduction of GPUs accelerated the depth migration of another real-world survey by a factor of 5. In both cases, a large fraction of the achieved gains were due to the porting strategy that privileges CPU-GPU A preliminary version of this work was published at the 21st International Symposium on Computer Architecture and High Performance Computing, SBAC-PAD 2009. 123 Int J Parallel Prog (2012) 40:290-312 291cooperation instead of pure GPU speed. Another strategy that contributed to the high gains was GPU overloading by multiple MPI processes. To cope with the heterogeneous load of real-world data, we developed a dynamic load balancing scheme that significantly speeds up computation.

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“…This article details a previously published study [15] and extends its scope from time migration to time and depth migration.…”

Section: Related Workmentioning

confidence: 93%

Accelerating Time and Depth Seismic Migration by CPU and GPU Cooperation

Panetta

Teixeira

Filho

et al. 2011

Int J Parallel Prog

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“…Panetta et al [11] ported the Kirchhoff Migration on a cluster of GPUs. They used Tesla C1060 GPU which does not have Read-Only Data Cache, so they parallelized the migration with CUDA using global memory without considering to fetch the data from a memory with less access latency.…”

Section: Related Workmentioning

confidence: 99%

Accelerating Kirchhoff Migration on GPU Using Directives

Hugues²,

Calandra³

et al. 2014

2014 First Workshop on Accelerator Programming Using Directives

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Accelerators offer the potential to significantly improve the performance of scientific applications when offloading compute intensive portions of programs to the accelerators. However, effectively tapping their full potential is difficult owing to the programmability challenges faced by the users when mapping computation algorithms to the massively parallel architectures such as GPUs.Directive-based programming models offer programmers an option to rapidly create prototype applications by annotating region of code for offloading with hints to the compiler. This is critical to improve the productivity in the production code. In this paper, we study the effectiveness of a high-level directivebased programming model, OpenACC, for parallelizing a seismic migration application called Kirchhoff Migration on GPU architecture. Kirchhoff Migration is a real-world production code in the Oil & Gas industry. Because of its compute intensive property, we focus on the computation part and explore different mechanisms to effectively harness GPU's computation capabilities and memory hierarchy. We also analyze different loop transformation techniques in different OpenACC compilers and compare their performance differences. Compared to one socket (10 CPU cores) on the experimental platform, one GPU achieved a maximum speedup of 20.54x and 6.72x for interpolation and extrapolation kernel functions.

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“…Additionally, there're many previous works [16][17] [18] taking advantages of CPU overlapping to hide CPU execution. They enlighten us to assign proper tasks to CPU and hide the cost time.…”

Section: Related Workmentioning

confidence: 99%

CLSIFT: An Optimization Study of the Scale Invariance Feature Transform on GPUs

Wang

Zhang

Guoping³

et al. 2013

2013 IEEE 10th International Conference on High Performance Computing and Communications &Amp; 2013 IEEE International Conferen

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Scale Invariance Feature Transform (SIFT) is quite suitable for image matching because of its invariance to image scaling, rotation and slight changes in illumination or viewpoint. However, due to high computation complexity it's technically challenging to deploy SIFT in real time application situations. To address this problem, we propose CLSIFT, an OpenCL based highly speeded up and performance portable SIFT solution. Important optimization techniques employed in CLSIFT such as: (1) For less global memory traffic, independent logical functions are merged into the same kernel to reuse data.(2) loop buffers are introduced in for data and intermediate results reusing.(3)Task queue used to schedule threads in the same branch to remove branch divergences. (4) Data partition is based on the statics patterns for workload balance among workgroups. (5) Overlap of CPU time and better parallel strategies are used too. With all mentioned efforts, CLSIFT processes lena.jpg at 74.2 FPS and 43.4FPS respectively on NVidia and AMD GPUS, much higher than CPU's nearly 10 FPS and the known fastest SIFTGPU's 39.8 FPS and 13FPS. Moreover in a quantitative comparison approach we analyze those successful strategies beating SIFTGPU, a famous existing GPU implementation. Additionally, we observe and conclude that NVidia GPU achieves better occupancy and performance due to some factors. Finally, we summarize some techniques and empirical guiding principles that may be shared by other applications on GPU.

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Accelerating Kirchhoff Migration by CPU and GPU Cooperation

Cited by 25 publications

References 7 publications

Accelerating Time and Depth Seismic Migration by CPU and GPU Cooperation

Accelerating Time and Depth Seismic Migration by CPU and GPU Cooperation

Accelerating Kirchhoff Migration on GPU Using Directives

CLSIFT: An Optimization Study of the Scale Invariance Feature Transform on GPUs

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