UPC++ v1.0 Programmer’s Guide, Revision 2020.3.0

UPC++ is a C++11 library that provides Partitioned Global Address Space (PGAS) programming. It is designed for writing parallel programs that run efficiently and scale well on distributed-memory parallel computers. The PGAS model is single program, multiple-data (SPMD), with each separate constituent process having access to local memory as it would in C++. However, PGAS also provides access to a global address space, which is allocated in shared segments that are distributed over the processes. UPC++ provides numerous methods for accessing and using global memory. In UPC++, all operations that access remote memory are explicit, which encourages programmers to be aware of the cost of communication and data movement. Moreover, all remote-memory access operations are by default asynchronous, to enable programmers to write code that scales well even on hundreds of thousands of cores

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

While many of the architectural details of future exascale-class high performance computer systems are still a matter of intense research, there appears to be a general consensus that they will be strongly heterogeneous, featuring "standard" as well as "accelerated" resources. Today, such resources are available as multicore processors, graphics processing units (GPUs), and other accelerators such as the Intel Xeon Phi. Any software infrastructure that claims usefulness for such environments must be able to meet their inherent challenges: massive multi-level parallelism, topology, asynchronicity, and abstraction. The "General, Hybrid, and Optimized Sparse Toolkit" (GHOST) is a collection of building blocks that targets algorithms dealing with sparse matrix representations on current and future large-scale systems. It implements the "MPI+X" paradigm, has a pure C interface, and provides hybrid-parallel numerical kernels, intelligent resource management, and truly heterogeneous parallelism for multicore CPUs, Nvidia GPUs, and the Intel Xeon Phi. We describe the details of its design with respect to the challenges posed by modern heterogeneous supercomputers and recent algorithmic developments. Implementation details which are indispensable for achieving high efficiency are pointed out and their necessity is justified by performance measurements or predictions based on performance models. The library code and several applications are available as open source. We also provide instructions on how to make use of GHOST in existing software packages, together with a case study which demonstrates the applicability and performance of GHOST as a component within a larger software stack.Comment: 32 pages, 11 figure

Statistical Regression Methods for GPGPU Design Space Exploration

General Purpose Graphics Processing Units (GPGPUs) have leveraged the performance and power efficiency of today\u27s heterogeneous systems to usher in a new era of innovation in high-performance scientific computing. These systems can offer significantly high performance for massively parallel applications; however, their resources may be wasted due to inefficient tuning strategies. Previous application tuning studies pre-dominantly employ low-level, architecture specific tuning which can make the performance modeling task difficult and less generic. In this research, we explore the GPGPU design space featuring the memory hierarchy for application tuning using regression-based performance prediction framework and rank the design space based on the runtime performance. The regression-based framework models the GPGPU device computations using algorithm characteristics such as the number of floating-point operations, total number of bytes, and hardware parameters pertaining to the GPGPU memory hierarchy as predictor variables. The computation component regression models are developed using several instrumented executions of the algorithms that include a range of FLOPS-to-Byte requirement. We validate our model with a Synchronous Iterative Algorithm (SIA) set that includes Spiking Neural Networks (SNNs) and Anisotropic Diffusion Filtering (ADF) for massive images. The highly parallel nature of the above mentioned algorithms, in addition to their wide range of communication-to-computation complexities, makes them good candidates for this study. A hierarchy of implementations for the SNNs and ADF is constructed and ranked using the regression-based framework. We further illustrate the Synchronous Iterative GPGPU Execution (SIGE) model on the GPGPU-augmented Palmetto Cluster. The performance prediction framework maps appropriate design space implementation for 4 out of 5 case studies used in this research. The final goal of this research is to establish the efficacy of the regression-based framework to accurately predict the application kernel runtime, allowing developers to correctly rank their design space prior to the large-scale implementation

High Performance Solutions for Big-data GWAS

In order to associate complex traits with genetic polymorphisms, genome-wide association studies process huge datasets involving tens of thousands of individuals genotyped for millions of polymorphisms. When handling these datasets, which exceed the main memory of contemporary computers, one faces two distinct challenges: 1) Millions of polymorphisms and thousands of phenotypes come at the cost of hundreds of gigabytes of data, which can only be kept in secondary storage; 2) the relatedness of the test population is represented by a relationship matrix, which, for large populations, can only fit in the combined main memory of a distributed architecture. In this paper, by using distributed resources such as Cloud or clusters, we address both challenges: The genotype and phenotype data is streamed from secondary storage using a double buffer- ing technique, while the relationship matrix is kept across the main memory of a distributed memory system. With the help of these solutions, we develop separate algorithms for studies involving only one or a multitude of traits. We show that these algorithms sustain high-performance and allow the analysis of enormous datasets.Comment: Submitted to Parallel Computing. arXiv admin note: substantial text overlap with arXiv:1304.227