A hybrid CPU/GPU approach for optimizing sorting throughput

Gowanlock, Michael; Karsin, Benjamin

doi:10.1016/j.parco.2019.01.004

Cited by 15 publications

(10 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is well documented that PCIe data transfers are a bottlebeck in GPU computing (Fujii et al, 2013;Van Werkhoven et al, 2014;Gowanlock and Karsin, 2019). In the case of the L-S algorithm, transferring periodogram(s) from the GPU back to the host requires a non-negligible amount of time.…”

Section: Transferring the Periodogram To The Hostmentioning

confidence: 99%

“…In the case of the L-S algorithm, transferring periodogram(s) from the GPU back to the host requires a non-negligible amount of time. To reduce this bottleneck, we employ the methods in Gowanlock and Karsin (2019) that reduce the overhead of performing host/device data transfers. We give a brief overview of the data transfer method here, but refer the interested reader to Gowanlock and Karsin (2019) for more detail.…”

Section: Transferring the Periodogram To The Hostmentioning

confidence: 99%

“…To reduce this bottleneck, we employ the methods in Gowanlock and Karsin (2019) that reduce the overhead of performing host/device data transfers. We give a brief overview of the data transfer method here, but refer the interested reader to Gowanlock and Karsin (2019) for more detail. In short, when performing a call to cudaMemcpy to transfer data from the GPU back to the host, the driver must create a temporary pinned memory buffer which is required so that the GPU's direct memory access (DMA) engine can copy data directly to a memory location that is unable to swap to disk (i.e., it is pinned and not pageable).…”

Section: Transferring the Periodogram To The Hostmentioning

confidence: 99%

See 2 more Smart Citations

Fast Period Searches Using the Lomb-Scargle Algorithm on Graphics Processing Units for Large Datasets and Real-Time Applications

Gowanlock¹,

Kramer²,

Trilling³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Computing the periods of variable objects is well-known to be computationally expensive. Modern astronomical catalogs contain a significant number of observed objects. Therefore, even if the period ranges for particular classes of objects are well-constrained due to expected physical properties, periods must be derived for a tremendous number of objects. In this paper, we propose a GPU-accelerated Lomb-Scargle period finding algorithm that computes periods for single objects or for batches of objects as is necessary in many data processing pipelines. We demonstrate the performance of several optimizations, including comparing the use of shared and global memory GPU kernels and using multiple CUDA streams to copy periodogram data from the GPU to the host. Also, we quantify the difference between 32-bit and 64-bit floating point precision on two classes of GPUs, and show that the performance degradation of using 64-bit over 32-bit is greater on the CPU than a GPU designed for scientific computing. We find that the GPU algorithm achieves superior performance over the baseline parallel CPU implementation, achieving a speedup of up to 174.53×. The Vera C. Rubin Observatory will carry out the Legacy Survey of Space and Time (LSST). We perform an analysis that shows we can derive the rotation periods of batches of Solar System objects at LSST scale in near real-time, which will be employed in a future LSST event broker. All source code has been made publicly available.

show abstract

Section: Transferring the Periodogram To The Hostmentioning

confidence: 99%

Section: Transferring the Periodogram To The Hostmentioning

confidence: 99%

Section: Transferring the Periodogram To The Hostmentioning

confidence: 99%

See 1 more Smart Citation

Fast Period Searches Using the Lomb-Scargle Algorithm on Graphics Processing Units for Large Datasets and Real-Time Applications

Gowanlock¹,

Kramer²,

Trilling³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Parallel computing devices targeted by OpenCL include PCs, servers, handheld devices, and embedded platforms. 7 It acts on the heterogeneous platform in the form of API, and the computing resources of multiple microprocessors, graphics processors, reconfigurable hardware, digital signal processors, and other platforms can be regarded as computing units for scheduling and sharing, so as to realize parallel computing across hardware architectures. 8 This thesis fully considers the cross-platform characteristics and aims to study how to design a parallel algorithm that can be processed on different GPU platforms with the help of the OpenCL programming model to solve the performance bottleneck of the radix sorting algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In response to the limitations of CUDA on heterogeneous platforms, Apple introduced a public, open cross‐platform parallel computing standard called Open Computing Language (OpenCL). Parallel computing devices targeted by OpenCL include PCs, servers, handheld devices, and embedded platforms 7 . It acts on the heterogeneous platform in the form of API, and the computing resources of multiple microprocessors, graphics processors, reconfigurable hardware, digital signal processors, and other platforms can be regarded as computing units for scheduling and sharing, so as to realize parallel computing across hardware architectures 8 …”

Section: Introductionmentioning

confidence: 99%

A radix sorting parallel algorithm suitable for graphic processing unit computing

Xiao

Guo

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

Radix sorting is an essential basic data processing operation in many computer fields. It has important practical significance to accelerate its performance through Graphic Processing Unit (GPU). The heterogeneous parallel computing technology attracts much attention and is widely applied for its effective computation efficiency and parallel real-time data processing capability. Taking advantage of the parallelism of GPU in numerical computation processing, a parallelization design method of the Binary_Least Significant Digit (LSD) first Radix Sorting (B_LSD_RS) algorithm based on Open Computing Language (OpenCL) is proposed. The radix sorting algorithm is divided into multiple kernel tasks, and the kernels are sequentially controlled by the event information transfer. The parallel algorithm is implemented and verified on the GPU + CPU heterogeneous platform. The experimental results show that compared with the performance of the B_LSD_RS sequential algorithm based on AMD Ryzen5 1600X CPU, B_LSD_RS parallel algorithm based on Open Multi-Processing (OpenMP) and B_LSD_RS parallel algorithm based on Compute Unified Device Architecture (CUDA), the B_LSD_RS parallel algorithm based on OpenCL obtained 28.86 times, 11.01 times and 2.14 times speedup in the NVIDIA GTX 1070 computing platform respectively, not only achieves high performance but also achieves performance portability among different GPU computing platforms.

show abstract

A Survey on Heterogeneous CPU–GPU Architectures and Simulators

Alaei,

Yazdanpanah

2024

Concurrency and Computation

View full text Add to dashboard Cite

Heterogeneous architectures are vastly used in various high performance computing systems from IoT‐based embedded architectures to edge and cloud systems. Although heterogeneous architectures with cooperation of CPUs and GPUs and unified address space are increasingly used, there are still a lot of open questions and challenges regarding the design of these architectures. For evaluation, validation and exploration of next generation of heterogeneous CPU–GPU architectures, it is essential to use unified heterogeneous simulators for analyzing the execution of CPU–GPU workloads. This article presents a systematic review on challenges of heterogeneous CPU–GPU architectures with covering a diverse set of literatures on each challenge. The main considered challenges are shared resource management, network interconnections, task scheduling, energy consumption, and programming model. In addition, in this article, the state‐of‐the‐art of heterogeneous CPU–GPU simulation platforms is reviewed. The structure and characteristics of five cycle‐accurate heterogeneous CPU–GPU simulators are described and compared. We perform comprehensive discussions on the methodologies and challenges of designing high performance heterogeneous architectures. Moreover, for developing efficient heterogeneous CPU–GPU simulators, some recommendations are presented.

show abstract

A hybrid CPU/GPU approach for optimizing sorting throughput

Cited by 15 publications

References 37 publications

Fast Period Searches Using the Lomb-Scargle Algorithm on Graphics Processing Units for Large Datasets and Real-Time Applications

Fast Period Searches Using the Lomb-Scargle Algorithm on Graphics Processing Units for Large Datasets and Real-Time Applications

A radix sorting parallel algorithm suitable for graphic processing unit computing

A Survey on Heterogeneous CPU–GPU Architectures and Simulators

Contact Info

Product

Resources

About