Power Suit

The focus of this project began in utilizing hair as a vehicle to investigate rituals associated with mourning, grief, and the liminal space that exists between life and death. After an in depth search into the cultural, physical, spiritual, and material aspects of hair, I enacted process and labor to create a sculptural form that performs in response to grief. I chose to work with acrylic hair, as it exists in a space between the real and unreal, and visually is absorbed as the uncanny representation of the separated body. I wanted to discuss the body, while emphasizing its absence. What covers and shelters our skin? Clothing, our clothing outlast our bodies, our hair outlasts our bodies. I made a gown of hair, seven feet in length, standing five feet tall. I wove this gown of hair, and rendered it inaccessible, or rather impenetrable. The collar tight to the neck, doubling as a source of protection and suffocation. The dress, a queen’s gown. As little girls, women dream to become queens, not because of the beautiful clothing, but because of the absolute positions of power, control, authority, and respect. This piece “Power Suit” is an attempt to take back the power and control that grief inflicts over the bodies that are left living in the wake of loss

Will the exploratory behavior of lobsters decrease as they become familiar with their environment?

Previous studies have shown that most lobsters have a home range in which they reside on a daily basis. The tendency for lobsters to reside in a particular area suggests that they have the ability to learn the characteristics of an area using exploratory behavior. We hypothesize that the exploratory behavior of juvenile lobsters will decrease as time spent in a novel environment increases; specifically exploratory behavior will decrease as the lobsters continuously learn the environment. Exploratory activity of juvenile lobsters was monitored in six lobsters using two separate maze complexities. Lobsters were video recorded and activity was measured based on the distance traveled each day. Lobsters were kept in the maze for ten days; three lobsters were tested in the simple maze and three were tested in the complex maze. A lobster tested in the simple maze followed our hypothesis and showed a continuous decline in activity for several days (activity decreased from 260.55 cms/day to 45.8 cms/day by Day 7) before reaching a constant baseline level. Another lobster tested in the simple maze was only active during the night and showed a steady decline in nighttime activity. Only one of the lobsters tested in the complex maze showed any decline in activity. Overall, these results suggest that lobsters are able to learn at least some features of a simple maze within seven days and that lobsters need far more than ten days to learn the environment of a more complex maze environment

FASTCUDA: Open Source FPGA Accelerator & Hardware-Software Codesign Toolset for CUDA Kernels

Using FPGAs as hardware accelerators that communicate with a central CPU is becoming a common practice in the embedded design world but there is no standard methodology and toolset to facilitate this path yet. On the other hand, languages such as CUDA and OpenCL provide standard development environments for Graphical Processing Unit (GPU) programming. FASTCUDA is a platform that provides the necessary software toolset, hardware architecture, and design methodology to efficiently adapt the CUDA approach into a new FPGA design flow. With FASTCUDA, the CUDA kernels of a CUDA-based application are partitioned into two groups with minimal user intervention: those that are compiled and executed in parallel software, and those that are synthesized and implemented in hardware. A modern low power FPGA can provide the processing power (via numerous embedded micro-CPUs) and the logic capacity for both the software and hardware implementations of the CUDA kernels. This paper describes the system requirements and the architectural decisions behind the FASTCUDA approach

CU2CL: A CUDA-to-OpenCL Translator for Multi- and Many-core Architectures

The use of graphics processing units (GPUs) in high-performance parallel computing continues to become more prevalent, often as part of a heterogeneous system. For years, CUDA has been the de facto programming environment for nearly all general-purpose GPU (GPGPU) applications. In spite of this, the framework is available only on NVIDIA GPUs, traditionally requiring reimplementation in other frameworks in order to utilize additional multi- or many-core devices. On the other hand, OpenCL provides an open and vendorneutral programming environment and runtime system. With implementations available for CPUs, GPUs, and other types of accelerators, OpenCL therefore holds the promise of a “write once, run anywhere” ecosystem for heterogeneous computing. Given the many similarities between CUDA and OpenCL, manually porting a CUDA application to OpenCL is typically straightforward, albeit tedious and error-prone. In response to this issue, we created CU2CL, an automated CUDA-to- OpenCL source-to-source translator that possesses a novel design and clever reuse of the Clang compiler framework. Currently, the CU2CL translator covers the primary constructs found in CUDA runtime API, and we have successfully translated many applications from the CUDA SDK and Rodinia benchmark suite. The performance of our automatically translated applications via CU2CL is on par with their manually ported countparts

Strategies for protecting intellectual property when using CUDA applications on graphics processing units

Recent advances in the massively parallel computational abilities of graphical processing units (GPUs) have increased their use for general purpose computation, as companies look to take advantage of big data processing techniques. This has given rise to the potential for malicious software targeting GPUs, which is of interest to forensic investigators examining the operation of software. The ability to carry out reverse-engineering of software is of great importance within the security and forensics elds, particularly when investigating malicious software or carrying out forensic analysis following a successful security breach. Due to the complexity of the Nvidia CUDA (Compute Uni ed Device Architecture) framework, it is not clear how best to approach the reverse engineering of a piece of CUDA software. We carry out a review of the di erent binary output formats which may be encountered from the CUDA compiler, and their implications on reverse engineering. We then demonstrate the process of carrying out disassembly of an example CUDA application, to establish the various techniques available to forensic investigators carrying out black-box disassembly and reverse engineering of CUDA binaries. We show that the Nvidia compiler, using default settings, leaks useful information. Finally, we demonstrate techniques to better protect intellectual property in CUDA algorithm implementations from reverse engineering