GPUs play an increasingly important role in high-performance computing. While developing naive code is straightforward, optimizing massively parallel applications requires deep understanding of the underlying architecture. The developer must struggle with complex index calculations and manual memory transfers. This article classifies memory access patterns used in most parallel algorithms, based on Berkeley’s Parallel “Dwarfs.” It then proposes the MAPS framework, a device-level memory abstraction that facilitates memory access on GPUs, alleviating complex indexing using on-device containers and iterators. This article presents an implementation of MAPS and shows that its performance is comparable to carefully optimized implementations of real-world applications.
Rubin, Eri, et al. ["MAPS: Optimizing Massively Parallel Applications Using Device-Level Memory Abstraction."](http://dl.acm.org/citation.cfm?id=2680544) ACM Transactions on Architecture and Code Optimization (TACO) 11.4 (2014): 44.
Initially introduced as special-purpose accelerators for graphics applications, graphics processing units (GPUs) have now emerged as general purpose computing platforms for a wide range of applications. To address the requirements of these applications, modern GPUs include sizable hardware-managed caches. However, several factors, such as unique architecture of GPU, rise of CPU-GPU heterogeneous computing, etc., demand effective management of caches to achieve high performance and energy efficiency. Recently, several techniques have been proposed for this purpose. In this paper, we survey several architectural and system-level techniques proposed for managing and leveraging GPU caches. We also discuss the importance and challenges of cache management in GPUs. The aim of this paper is to provide the readers insights into cache management techniques for GPUs and motivate them to propose even better techniques for leveraging the full potential of caches in the GPUs of tomorrow.
Sparsh Mittal, “A Survey Of Techniques for Managing and Leveraging Caches in GPUs”, Journal of Circuits, Systems, and Computers (JCSC), vol. 23, no. 8, 2014. WWW
The wide majority of current state-of-the-art compressed GPU volume renderers are based on block-transform coding, which is susceptible to blocking artifacts, particularly at low bit-rates. In this paper the authors address the problem for the first time, by introducing a specialized deferred filtering architecture working on block-compressed data and including a novel deblocking algorithm. The architecture efficiently performs high quality shading of massive datasets by closely coordinating visibility- and resolution-aware adaptive data loading with GPU-accelerated per-frame data decompression, deblocking, and rendering. A thorough evaluation including quantitative and qualitative measures demonstrates the performance of our approach on large static and dynamic datasets including a massive 512^4 turbulence simulation (256GB), which is aggressively compressed to less than 2 GB, so as to fully upload it on graphics board and to explore it in real-time during animation.
(Fabio Marton, José Antonio Iglesias Guitián, Jose Díaz and Enrico Gobbetti: “Real-time deblocked GPU rendering of compressed volumes”. Proc. 19th International Workshop on Vision, Modeling and Visualization (VMV), pp. 167-174, Oct. 2014. [WWW])
PARALUTION is a library for sparse iterative methods which can be performed on various parallel devices, including multi-core CPU, GPU (CUDA and OpenCL) and Intel Xeon Phi. The new 0.8.0 release provides the following extra features:
- Complex support
- TNS, Variable preconditioner
- BiCGStab(l), QMRCGStab, FCG solvers
- RS and PairWise AMG
- SIRA eigenvalue solver
- Replace/Extract column/row functions
- Stencil computation
For details, visit http://www.paralution.com.
The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific applications in Bioinformatics, Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions, which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a massive exploration of perturbed conditions of the BCC, carried out with one-dimensional and bi-dimensional parameter sweep analysis, and show that GPU-accelerated parallel simulations of this model can increase the computational performances up to a 181× speedup compared to the corresponding sequential simulations.
(Cazzaniga P., Nobile M.S., Besozzi D., Bellini M., Mauri G.: “Massive exploration of perturbed conditions of the blood coagulation cascade through GPU parallelization”. BioMed Research International, vol. 2014. [DOI])
This webinar provides an overview of the improved analysis performance tools available in CUDA 6.0 and key optimization strategies for compute, latency and memory bound problems. The webinar includes techniques for ensuring peak utilization of CUDA cores, how to improve branching efficiency, intrinsic functions and loop unrolling. Optimal access patterns for global and shared memory are presented, including a comparison between the Fermi and Kepler architectures. To view the webinar go to: http://acceleware.com/blog/webinar-essential-cuda-optimization-techniques
Developed in partnership with NVIDIA, this hands-on four day course will teach you how to write and optimize applications that fully leverage the multi-core processing capabilities of the GPU. This course will have a finance focus. Commonly used algorithms such as random number generation and Monte Carlo simulations will be used and profiled in examples. A background in finance is not necessary. For more information please visit: http://acceleware.com/training/988
CUDPP release 2.2 is a feature release that adds a new parallel primitive and improves some existing primitives. We have added cudppSuffixArray, a parallel skew algorithm (SA) implementation that computes the suffix array of a string. This suffix array primitive is now used in burrowsWheelerTransform, delivering better performance than CUDPP 2.1’s use of cudppStringSort. The new BWT is further used in cudppCompress, which is now faster than the original parallel compression and supports compression of text containing all possible unsigned char values. Some bugs in cudppMoveToFrontTransform and cudppStringSort have also been fixed. OS X users might also be interested in how we supported the use of OS X’s clang compiler in OS X Mavericks (10.9).
This hands-on four day course teaches how to write and optimize applications that fully leverage the multi-core processing capabilities of the GPU. More details and registration: http://acceleware.com/training/986
Hybrid Fortran is an Open Source directive based extension for the Fortran language. It is a way for HPC programmers to keep writing Fortran code like they are used to – only now with GPGPU support. It achieves performance portability by allowing different storage orders and loop structures for the CPU and GPU version. All computational code stays the same as in the respective CPU version, e.g. it can be kept in a low dimensionality even when the GPU version needs to be privatised in more dimensions in order to achieve a speedup. Hybrid Fortran takes care of the necessary transformations at compile-time (so there is no runtime overhead). A (python based) preprocessor parses these annotations together with the Fortran user code structure, declarations, accessors and procedure calls, and then writes separate versions of the code – once for CPU with OpenMP parallelization and once for GPU with CUDA Fortran. More details: http://typhooncomputing.com/?p=416