Adapting the interior point method for the solution of LPs on serial, coarse grain parallel and massively parallel computers

In this paper we describe a unified scheme for implementing an interior point algorithm (IPM) over a range of computer architectures. In the inner iteration of the IPM a search direction is computed using Newton's method. Computationally this involves solving a sparse symmetric positive definite (SSPD) system of equations. The choice of direct and indirect methods for the solution of this system, and the design of data structures to take advantage of serial, coarse grain parallel and massively parallel computer architectures, are considered in detail. We put forward arguments as to why integration of the system within a sparse simplex solver is important and outline how the system is designed to achieve this integration

Polly's Polyhedral Scheduling in the Presence of Reductions

The polyhedral model provides a powerful mathematical abstraction to enable effective optimization of loop nests with respect to a given optimization goal, e.g., exploiting parallelism. Unexploited reduction properties are a frequent reason for polyhedral optimizers to assume parallelism prohibiting dependences. To our knowledge, no polyhedral loop optimizer available in any production compiler provides support for reductions. In this paper, we show that leveraging the parallelism of reductions can lead to a significant performance increase. We give a precise, dependence based, definition of reductions and discuss ways to extend polyhedral optimization to exploit the associativity and commutativity of reduction computations. We have implemented a reduction-enabled scheduling approach in the Polly polyhedral optimizer and evaluate it on the standard Polybench 3.2 benchmark suite. We were able to detect and model all 52 arithmetic reductions and achieve speedups up to 2.21$\times$ on a quad core machine by exploiting the multidimensional reduction in the BiCG benchmark.Comment: Presented at the IMPACT15 worksho

Optimizing I/O for Big Array Analytics

Big array analytics is becoming indispensable in answering important scientific and business questions. Most analysis tasks consist of multiple steps, each making one or multiple passes over the arrays to be analyzed and generating intermediate results. In the big data setting, I/O optimization is a key to efficient analytics. In this paper, we develop a framework and techniques for capturing a broad range of analysis tasks expressible in nested-loop forms, representing them in a declarative way, and optimizing their I/O by identifying sharing opportunities. Experiment results show that our optimizer is capable of finding execution plans that exploit nontrivial I/O sharing opportunities with significant savings.Comment: VLDB201

Synthesis Optimization on Galois-Field Based Arithmetic Operators for Rijndael Cipher

A  series  of  experiments  has  been  conducted  to  show  that  FPGA synthesis  of  Galois-Field  (GF)  based  arithmetic  operators  can  be  optimized automatically  to  improve  Rijndael  Cipher  throughput.  Moreover,  it  has  been demonstrated  that  efficiency  improvement  in  GF  operators  does  not  directly correspond to the system performance at application level. The experiments were motivated by so many research works that focused on improving performance of GF  operators.  Each  of  the  variants  has  the  most  efficient  form  in  either  time (fastest) or space  (smallest occupied area) when implemented in FPGA chips. In fact,  GF  operators are not utilized  individually, but  rather integrated one to the others to  implement algorithms.  Contribution  of  this  paper  is  to  raise  issue  on GF-based  application  performance  and  suggest  alternative  aspects  that potentially  affect  it.  Instead  of  focusing  on  GF  operator  efficiency,  system characteristics are worth considered in optimizing application performance