How can we provide data where it is required and when it is required to the execution units of parallel hardware? Program transformations have been a focus to improve the performance of parallel computing, whereas data optimizations like data placement, data layout transformation, data migration and data replications are overlooked especially in compiler domain. We are proposing a methodology, Temporal Data Placement, that will schedule and place data in both time and space. The use of our methodology will enhance the performance of parallel systems significantly, and it can also be automated
Parallel architectures with physically distributed memory providing computing cycles and large amoun...
Recent years have seen an increasing number of scientists employ data parallel computing frameworks ...
International audienceNowadays, NUMA architectures are common in compute-intensive systems. Achievin...
Abstract. This paper shows how data placement optimisation tech-niques which are normally only found...
In parallel programs the most important improvements in execution times can be achieved by the optim...
In this paper, we study the data placement problem from a reorganization point of view. Effective...
Programming distributed-memory machines requires careful placement of data to balance the computatio...
Abstract. This paper describes a combination of methods which make interprocedural data placement op...
We study the data placement problem [1, 3], where the goal is to place data objects in xed capacity ...
The performance of a High Performance Parallel or Distributed Computation depends heavily on minimiz...
Abstract. In recent years, there has been a growing demand on the required resources in terms of com...
Todays scientific applications have huge data requirements, and these requirements continue to incre...
While parallel programming is needed to solve large-scale scientific applications, it is more diffic...
The memory system is a major bottleneck in achieving high performance and energy efficiency for vari...
We present a unified approach to locality optimization that employs both data and control transforma...
Parallel architectures with physically distributed memory providing computing cycles and large amoun...
Recent years have seen an increasing number of scientists employ data parallel computing frameworks ...
International audienceNowadays, NUMA architectures are common in compute-intensive systems. Achievin...
Abstract. This paper shows how data placement optimisation tech-niques which are normally only found...
In parallel programs the most important improvements in execution times can be achieved by the optim...
In this paper, we study the data placement problem from a reorganization point of view. Effective...
Programming distributed-memory machines requires careful placement of data to balance the computatio...
Abstract. This paper describes a combination of methods which make interprocedural data placement op...
We study the data placement problem [1, 3], where the goal is to place data objects in xed capacity ...
The performance of a High Performance Parallel or Distributed Computation depends heavily on minimiz...
Abstract. In recent years, there has been a growing demand on the required resources in terms of com...
Todays scientific applications have huge data requirements, and these requirements continue to incre...
While parallel programming is needed to solve large-scale scientific applications, it is more diffic...
The memory system is a major bottleneck in achieving high performance and energy efficiency for vari...
We present a unified approach to locality optimization that employs both data and control transforma...
Parallel architectures with physically distributed memory providing computing cycles and large amoun...
Recent years have seen an increasing number of scientists employ data parallel computing frameworks ...
International audienceNowadays, NUMA architectures are common in compute-intensive systems. Achievin...