Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …

The massive parallelism present in GPUs comes at the cost of reduced L1 and L2 cache
sizes per thread, leading to serious cache contention problems such as thrashing. Hence …

Data-intensive applications dominated by random accesses to large working sets fail to
utilize the computing power of modern processors. Graph random walk, an indispensable …

Modern Convolutional Neural Networks (CNNs) require a massive amount of convolution
operations. To address the overwhelming computation problem, Winograd and FFT fast …

Modern GPUs concurrently deploy thousands of threads to maximize thread level
parallelism (TLP) for performance. For some applications, however, maximized TLP leads to …

Unified virtual memory was introduced in modern GPUs to enable a new programming
model for programmers. This method manages memory pages between the GPU and CPU …

Existing OS techniques for homogeneous many-core systems make it simple for single and
multithreaded applications to migrate between cores. Heterogeneous systems do not benefit …

Latent Dirichlet Allocation (LDA) is a statistical approach for topic modeling with a wide
range of applications. Attracted by the exceptional computing and memory throughput …

Abstract Latent Dirichlet Allocation (LDA) is a statistical approach for topic modeling with a
wide range of applications. LDA can be subdivided into flatten model and hierarchical …

Parallel and heterogeneous systems are ubiquitous. Unfortunately, both require significant
complexity at the software level to the detriment of programmer productivity. To produce …