  This paper presents the Graph Analytics Repository for Designing
Next-generation Accelerators (GARDENIA), a benchmark suite for studying
irregular algorithms on massively parallel accelerators. Existing generic
benchmarks for accelerators have mainly focused on high performance computing
(HPC) applications with limited control and data irregularity, while available
graph analytics benchmarks do not apply state-of-the-art algorithms and/or
optimization techniques. GARDENIA includes emerging irregular applications in
big-data and machine learning domains which mimic massively multithreaded
commercial programs running on modern large-scale datacenters. Our
characterization shows that GARDENIA exhibits irregular microarchitectural
behavior which is quite different from structured workloads and
straightforward-implemented graph benchmarks.
