Challenges with Real-World Smartwatch based Audio Monitoring

Wednesday June 6th, 12-1PM @ BA5205

Speaker: Daniyal Liaqat
Title:
Challenges with Real-World Smartwatch based Audio Monitoring

Abstract:
Audio data from a microphone can be a rich source of information. The speech and audio processing community has explored using audio data to detect emotion, depression, Alzheimer’s disease and even children’s age, weight and height. The mobile community has looked at using smartphone based audio to detect coughing and other respiratory sounds and help predict students’ GPA.
However, audio data from these studies tends to be collected in more controlled environments using well placed, high quality microphones or from phone calls. Applying these kinds of analyses to continuous and in-the-wild audio could have tremendous applications, particularly in the context of health monitoring. As part of a health monitoring study, we use smartwatches to collect in-the-wild audio from real patients. In this paper we characterize the quality of the audio data we collected. Our findings include that the smartwatch based audio is good enough to discern speech and respiratory sounds. However, extracting these sounds is difficult because of the wide variety of noise in the signal and current tools perform poorly at dealing with this noise. We also find that the quality of the microphone allows annotators to differentiate the source of speech and coughing, which adds another level of complexity to analyzing this audio.

Heterogeneous GPU reallocation

Wednesday July 5th, 1-2PM @ BA5205

Speaker: James Gleeson

Title:
Heterogeneous GPU reallocation

Abstract:
Emerging cloud markets like spot markets and batch computing services scale up services at the granularity of whole VMs. In this paper, we observe that GPU workloads underutilize GPU device memory, leading us to explore the benefits of reallocating heterogeneous GPUs within existing VMs. We outline approaches for upgrading and downgrading GPUs for OpenCL GPGPU workloads, and show how to minimize the chance of cloud operator VM termination by maximizing the heterogeneous environments in which applications can run.

Bio:
James is a PhD student under Eyal de Lara.  He has done research in mobile security for both physical and software attacks on Android phones.  His current research interests are in heterogeneous computing in data centers.

Crane: Fast and Migratable GPU Passthrough for OpenCL applications

Wednesday May 17th, 12-1PM @ BA5205

Speaker: James Gleeson

Title:
Crane: Fast and Migratable GPU Passthrough for OpenCL applications

Abstract:
General purpose GPU (GPGPU) computing in virtualized environments leverages PCI passthrough to achieve GPU performance comparable to bare-metal execution. However, GPU passthrough prevents service administrators from performing virtual machine migration between physical hosts.
Crane is a new technique for virtualizing OpenCL-based GPGPU computing that achieves within 5.25% of passthrough GPU performance while supporting VM migration. Crane interposes a virtualization-aware OpenCL library that makes it possible to reclaim and subsequently reassign physical GPUs to a VM without terminating the guest or its applications. Crane also enables continued GPU operation while the VM is undergoing live migration by transparently switching between GPU passthrough operation and API remoting.
 

Bio:
James is a PhD student under Eyal de Lara.  He has done research in mobile security for both physical and software attacks on Android phones.  His current research interests are in heterogeneous computing in data centers.

Accelerating Complex Data Transfer for Cluster Computing

Friday June 10th, 12-1PM @ BA5205

Speaker: Alexey

Title:
Accelerating Complex Data Transfer for Cluster Computing

Abstract:
The ability to move data quickly between the nodes of a distributed system is important for the performance of cluster computing frameworks, such as Hadoop and Spark. We show that in a cluster with modern networking technology data serialization is the main bottleneck and source of overhead in the transfer of rich data in systems based on high-level programming languages such as Java. We propose a new data transfer mechanism that avoids serialization altogether by using a shared cluster-wide address space to store data. The design and a prototype implementation of this approach are described. We show that our mechanism is significantly faster than serialized data transfer, and propose a number of possible applications for it.

Bio:
Alexey Khrabrov is a 1st year PhD student at University of Toronto, under the supervision of prof. Eyal de Lara. His research interests lie in the area of performance of distributed systems. His current work focuses on leveraging modern network technologies and designing new programming models to improve data transfer performance in cluster computing systems.