Tensor computations underlie modern scientific computing and deep learning. A number of
tensor frameworks emerged varying in execution model, hardware support, memory …

Adapting applications to optimally utilize available hardware is no mean feat: the plethora of
choices for optimization techniques are infeasible to maximize manually. To this end, auto …

A source-to-source compiler is a type of translator that accepts the source code of a program
written in a programming language as its input and produces an equivalent source code in …

HPC is a heterogeneous world in which host and device code are interleaved throughout
the application. Given the significant performance advantage of accelerators, device code …

Object-oriented programming has long been regarded as too inefficient for SIMD high-
performance computing, despite the fact that many important HPC applications have an …

In the present scenario, high-performance computing needs more attention towards
multicore computing. While designing the CPU, we need to consider hardware for …

Data management systems commonly use bitmap indices to increase the efficiency of
querying scientific data. Bitmaps are usually highly compressible and can be queried …

High-Performance computing provides a new infrastructure for scientific calculation and its
simulation. However, unbalanced load distribution among the processors causes a …

Data management systems commonly use bitmap indices to increase the efficiency of
querying scientific data. Bitmaps are usually highly compressible and can be queried …

Optimizing GPU programs by tuning execution parameters is essential to realizing the full
performance potential of GPU hardware. However, many of these optimizations do not …