Irregular Iteration Computing

This is specially challenging for irregular workloads, where each iteration's work is data dependent and shows control and memory divergence. tGi , tGi1 . . . for the GPU and tC0 , tC1 . . . tCi'1 , tCi , tCi1 . . . for each CPU core. Each computing device at its ith interval, tGi or tCi , gets a chunk of iterations of size GtGi

that obtained at run time for the irregular parts of the loop to ef-fectively partition its iterations. Completely afne and completely irregular computations are naturally expressed as special instances of the framework. The developed compiler framework is based on integer setsmapsslices and has two important advantages

In this article, we develop a reservoir computing RC-based iterative learning method for recovering missing data in irregular time series generated by dynamical systems and networks. In particular, we formulate this learning task as a fixed-point iterative learning problem and develop a training procedure using an RC network RCN.

Parallel computing promises several orders of magnitude increase in our ability to solve realistic computationally-intensive problems, but relies on their efficient mapping and execution on large-scale multiprocessor architectures. Unfortunately, many important applications are irregular and dynamic in nature, making their

Frameworks to Irregular Applications aka Math for Irregular Codes loops,quotIn Advances in Languages and Compilers for Parallel Computing, 1990. -Wolf amp Lam 91 Wolf and Lam, quotA Data Locality Optimizing Kelly and Pugh, quotA unifying framework for iteration reordering transformations,quot In IEEE First International Conference on

In the field of reservoir numerical simulation, irregular iteration problem is one of the most important reasons to affect the efficiency of large-scale computing. Memory access patterns can't be determined at compiler-time, which brings us difficulties to parallel. This library uses InspectorExecutor model based on distributed clusters, and improves parallel efficiency utilizing alternate

between the cost of dynamically computing data features and only utilizing trivially known features for prediction. A. Training The training abstraction, as shown in Fig. 2, requires three inputs GPU irregular workload kernels, the kernels of interest which exhibit irregular patterns and vary in performance depending on the shape of the data.

100th iteration. Dynamic irregularities severely limit the efciency of GPU computing for many applications. As shown in Figure 2, remov-ing the dynamic irregularities may improve the performance of a set of GPU applications and kernels detailed in Section 7 by a factor of 1.4 to 5.3. There have been some recent explorations on the

In the field of reservoir numerical simulation, irregular iteration problem is one of the most important reasons to affect the efficiency of large-scale computing. Memory access patterns can't be determined at compiler-time, which brings us difficulties to parallel.

for irregular applications and there is a need for exible, robust, and eec-tive programming support. Parallel programming models and environments The hierarchical method alternates iteration steps of the Jacobi method to solve the energy system 3 with a re-computation of the quadtree and the