Research

Computer Architecture / Interconnection Networks

High-radix topologies in large-scale networks provide low network diameter and high path diversity, but the idle power from high-speed links results in energy inefficiency,especially at low traffic load. In this work, we exploit the high path diversity and non-minimal adaptive routing in high-radix topologies to consolidate traffic to a smaller number of links to enable more network channels to be power-gated. Inparticular, we propose TCEP (Traffic Consolidation for Energy-Proportional high-radix networks), a distributed, proactive power management mechanism for large-scale networks thatachieves energy-proportionality by proactively power-gating network channels through traffic consolidation.

Deep Learning Accelerators

Data parallelism is commonly used to accelerate training for Convolutional NeuralNetworks (CNN) where input batch is distributed across the multiple workers; however, the communication time of weight gradients can limit scalability for moderate batch size. In this work, we propose multi-dimensional parallel training (MPT) of convolution layers by exploiting both data parallelism and intra-tile parallelism available in Winograd transformed convolution.Workers are organized across two dimensions – one dimension exploiting intra-tile parallelism while the other dimension exploits data parallelism. MPT reduces the amount of communication necessary for weight gradients since weight gradients are only communicated within the data parallelism dimension.

Scale-out systems

NUMA (non-uniform memory access) servers are commonly used in high-performance computing and datacenters. Within each server, a processor-interconnect (e.g.,Intel QPI, AMD HyperTransport) is used to communicate between the different sockets or nodes. In this work,we explore the impact of the processor-interconnect on overall performance – in particular, the performance unfairness caused by processor-interconnect arbitration. It is well known that locally-fair arbitration does not guarantee globally-fair bandwidth sharing as closer nodes receive more bandwidth in a multi-hop network. However, this work demonstrates that the opposite can occur in a commodity NUMA server where remote nodes receive higher bandwidth(and perform better). We analyze this problem and identify that this occurs because of external concentration used in router micro-architectures for processor-interconnect swithout globally-aware arbitration.

Hardware Security

The goal of rootkit is often to hide malicious software running on a compromised machine. While there has been significant amount of research done on different rootkits, we describe a new type of rootkit that is kernel-independent – i.e., no aspect of the kernel is modified and no code is added to the kernel address space to install the rootkit. In this work, we present PIkit – Processor-Interconnect rootkit that exploits the vulnerable hardware features within multi-socket servers that are commonly used in datacenters and high-performance computing. In particular, PIkit exploits the DRAM address mapping table structure that determines the destination node of a memory request packet in the processor interconnect. By modifying this mapping table appropriately, PIkit enables access to victim’s memory address region without proper permission.

Multi-GPU system

GPUs are being widely used to accelerate different workloads and multi-GPU systems can provide higher performance with multiple discrete GPUs interconnected together.However, there are two main communication bottlenecks in multi-GPU systems – accessing remote GPU memory and the communication between GPU and the host CPU. Recent advances in multi-GPU programming, including unified virtual addressing and unified memory from NVIDIA, has made programming simpler but the costly remote memory access still makes multi-GPU programming difficult. In order to overcome the communication limitations, we propose to leverage the memory network based on hybrid memory cubes (HMCs) to simplify multi-GPU memory management and improve programmability.

Mobile Systems for Detection & Intervention

Everyday Clinical Care through Interaction Sensing [CSCW’14]

Everyday Clinical Care through Interaction Sensing [CSCW’14]

Everyday Clinical Care through Interaction Sensing [CSCW’14]