  Outlier detection (OD) is a key learning task for finding rare and deviant
data samples, with many time-critical applications such as fraud detection and
intrusion detection. In this work, we propose TOD, the first tensor-based
system for efficient and scalable outlier detection on distributed multi-GPU
machines. A key idea behind TOD is decomposing complex OD applications into a
small collection of basic tensor algebra operators. This decomposition enables
TOD to accelerate OD computations by leveraging recent advances in deep
learning infrastructure in both hardware and software. Moreover, to deploy
memory-intensive OD applications on modern GPUs with limited on-device memory,
we introduce two key techniques. First, provable quantization speeds up OD
computations and reduces its memory footprint by automatically performing
specific floating-point operations in lower precision while provably
guaranteeing no accuracy loss. Second, to exploit the aggregated compute
resources and memory capacity of multiple GPUs, we introduce automatic
batching, which decomposes OD computations into small batches for both
sequential execution on a single GPU and parallel execution on multiple GPUs.
  TOD supports a diverse set of OD algorithms. Extensive evaluation on 11 real
and 3 synthetic OD datasets shows that TOD is on average 10.9x faster than the
leading CPU-based OD system PyOD (with a maximum speedup of 38.9x), and can
handle much larger datasets than existing GPU-based OD systems. In addition,
TOD allows easy integration of new OD operators, enabling fast prototyping of
emerging and yet-to-discovered OD algorithms.
