Two-Stage Least Squares Algorithms with QR Decomposition for Simultaneous Equations Models on Heterogeneous Multicore and Multi-GPU Systems

نویسندگان

  • Carla Ramiro
  • Jose-Juan López-Espín
  • Domingo Giménez
  • Antonio M. Vidal
چکیده

G21 Z̃22 Z̃23 Z̃24 W̃21 G31 Z̃32 Z̃33 Z̃34 W̃31 G41 G42 Z̃43 Z̃44 W̃41 G51 G52 Z̃53 Z̃54 W̃51 Z11 Z12 Z13 Z14 W11 G21 Z̃22 Z̃23 Z̃24 W̃21 G31 Z̃32 Z̃33 Z̃34 W̃31 G41 G42 Z̃43 Z̃44 W̃41 G51 G52 Z̃53 Z̃54 W̃51 Two-Stage Least Squares algorithms with QR decomposition for Simultaneous Equations Models on heterogeneous multicore and multi-GPU systems Carla Ramiroa, José J. López-Espínb, Domingo Giménezc and Antonio M. Vidala

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Towards a multifrontal QR factorization for heterogeneous architectures over runtime systems

During the last decade, computer architectures for high performance computing have considerably evolved toward heterogeneous systems equipped with different types of computational units and a higher number of cores per chips. An example of popular heterogeneous architectures widely adopted in the high performance computing domain are GPU-based systems. In the work presented in this talk we stud...

متن کامل

Enabling and Scaling Matrix Computations on Heterogeneous Multi-Core and Multi-GPU Systems

We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and multi-GPU systems to support dense matrix computations efficiently. The main idea is that we treat a heterogeneous system as a distributedmemory machine, and use a heterogeneous multi-level block cyclic distribution method to allocate data to the host and multiple GPUs to minimize communication. We ...

متن کامل

One-sided dense matrix factorizations on a multicore with multiple GPU accelerators in MAGMA1

One-sided dense matrix factorizations are important computational kernels in many scientific and engineering simulations. In this paper, we propose two extensions of both right-looking (LU and QR) and left-looking (Cholesky) factorization algorithms to utilize the computing power of current heterogeneous architectures. We first describe a new class of non-GPU-resident algorithms that factorize ...

متن کامل

One-sided Dense Matrix Factorizations on a Multicore with Multiple GPU Accelerators

One-sided dense matrix factorizations are important computational kernels in many scientific and engineering simulations. In this paper, we propose two extensions of both right-looking (LU and QR) and left-looking (Cholesky) one-sided factorization algorithms to utilize the computing power of current heterogeneous architectures. We first describe a new class of non-GPU-resident algorithms that ...

متن کامل

A scalable approach to solving dense linear algebra problems on hybrid CPU-GPU systems

Aiming to fully exploit the computing power of all CPUs and all GPUs on hybrid CPU-GPU systems to solve dense linear algebra problems, we design a class of heterogeneous tile algorithms to maximize the degree of parallelism, to minimize the communication volume, as well as to accommodate the heterogeneity between CPUs and GPUs. The new heterogeneous tile algorithms are executed upon our decentr...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012