Sparse Matrix-Vector Multiplication (SpMV) is an essential computation kernel for many data-
analytic workloads running in both supercomputers and data centers. The intrinsic …

The complexity and diversity of big data and AI workloads make understanding them difficult
and challenging. This paper proposes a new approachto modelling and characterizing big …

Several fundamental changes in technology indicate domain-specific hardware and
software co-design is the only path left. In this context, architecture, system, data …

Modern parallel architecture design has increasingly turned to throughput-oriented devices
to address concerns about energy efficiency and power consumption. However, graph …

For the architecture community, reasonable simulation time is a strong requirement in
addition to performance data accuracy. However, emerging big data and AI workloads are …

BigDataBench: A Dwarf-based Big Data and AI Benchmark Suite Page 1 IN STITU TE O F C
O M P U TIN GT E C H N O L O GY BigDataBench: A Dwarf-based Big Data and AI …

The complexity and diversity of big data and AI workloads make understanding them difficult
and challenging. This paper proposes a new approach to characterizing big data and AI …

Sparse matrix-vector multiplication, SpMV, can be a performance bottle-neck in iterative
solvers and algebraic eigenvalue problems. In this thesis, we present our sparse matrix …