GPGPU

In the last years the traditional ways to keep the increase of hardware performance to the rate predicted by the Moore's Law vanished. When uni-cores were the norm, hardware design was decoupled from the software stack thanks to a well defined ...

Finding Strongly Connected Components (SCC) of a directed graph is a fundamental graph problem. Many of the state-of-the-art sequential algorithms use depth-first search (DFS) to find SCCs. Since, in general DFS is hard to parallelize, researchers rely ...

We investigate applying general-purpose join algorithms to the triangle listing problem on heterogeneous systems that feature a multi-core CPU and multiple GPUs. In particular, we consider an out-of-core context where graph data are available on ...

Parallel accelerators such as GPUs are notoriously hard to program; exploiting their full performance potential is a job best left for ninja programmers. High-level programming languages coupled with optimizing compilers have been proposed to attempt to ...

Writing and optimizing programs for high performance on systems with Graphics Processing Units (GPUs) remains a challenging task even for expert programmers. A promising optimization technique is multi-stage programming -- evaluating parts of the ...

Heterogeneous architectures have experienced a great development thanks to their excellent cost/performance ratio and low power consumption. But heterogeneity significantly complicates both programming and efficient use of the resources. As a result, ...

We can now say that almost all future performance improvements will come from heterogeneous acceleration. But the reality of building successful software and platforms is that no one company or individual can create everything. That means we need to ...

Achieving optimal performance on heterogeneous computing systems requires a programming model that supports the execution of asynchronous, multi-stream, and out-of-order tasks in a shared memory environment. Asynchronous dependency-driven tasking is one ...

As GPUs become general purpose, they are outgrowing the coprocessor model and require convenient I/O abstractions such as files and network sockets. Recent studies have shown the benefits of native GPU I/O layers, in terms of both programmability and ...

Over a distinguished career, Regents Professor Karsten Schwan has made significant contributions across a diverse array of topics in computer systems, including operating systems for multi-core platforms, virtualization technologies, enterprise ...

Graphics Processing Units (GPUs) have gained the position of a main stream accelerator due to its low power footprint and massive parallelism. CUDA 6.0 onward, NVIDIA has introduced the Managed Memory capability which unifies the host and device memory ...

GPUs are an attractive target for data parallel stencil computations prevalent in scientific computing and image processing applications. Many tiling schemes, such as overlapped tiling and split tiling, have been proposed in past to improve the ...