The broad usage of accelerators, such as GPUs, faces two important challenges. Developing code for a new accelerator is expensive and unpredictable. Porting large parallel programs from Multiple Instruction Multiple Data (MIMD) CPUs to Single Instruction Multiple Thread (SIMT) GPUs involves significant effort that may or may not result in improved performance versus the CPU. This high activation energy to create new workloads introduces the second challenge: architects and systems researchers lack a diverse SIMT codebase to study new designs. To tackle these challenges, we introduce ThreadFuser, an analysis framework that efficiently and accurately predicts the performance of any pre-written MIMD program on SIMT hardware. ThreadFuser conducts thorough control and data flow analysis on dynamic CPU program traces, determining the impact of lock-step execution on CPU binaries. Thread-Fuser efficiently delivers accurate reports on a MIMD program's divergence and synchronization characteristics. Moreover, ThreadFuser seamlessly integrates with state-of-the-art GPU simulators to conduct detailed analyses and produce fine-grained performance measurements. We evaluate ThreadFuser on a diverse set of 36 CPU workloads, demonstrating the potential and challenges of executing MIMD code on a SIMT machine. We demonstrate ThreadFuser's potential to inform software development decisions and open new areas to explore in data-parallel hardware design.

Since the early days of computers, dividing a program into functions or subroutines has been a common way to manage complexity. Functions make programs easier to read, facilitate code reuse, and provide clean interfaces for separate compilation. However, function calls incur runtime overhead. We quantify the impact of this runtime overhead on GPUs and demonstrate that the register spills/fills required to maintain the function call application binary interface place significant bandwidth and capacity pressure on shared resources. To alleviate this overhead, we introduce Concurrency-Aware Register Stacks (CARS), a hardware mechanism that re-purposes segments of the GPU register file as a software-controlled hardware stack. CARS exploits the regularity in function prologue/epilogues to rename registers pushed to the stack with linear base + offset addressing, similar to the baseline GPU. Informed by lightweight call graph analysis and dynamic function behavior, CARS balances the space devoted to register stacks with the concurrency required to hide latency in GPUs. Without harming function-free programs, CARS improves the performance and energy efficiency of 22 function-calling applications by 26% and 28%, respectively, outperforming idealized GPUs with impractical resources.

Effective psychological interventions for anxiety disorders often include exposure to fearful situations. However, individuals with low self-efficacy may find such exposure too overwhelming. We created a vicarious experience in virtual reality, which enables observation of one’s experience from a first person perspective without actual performance and which might increase self-efficacy. With similarities to both traditional vicarious experiences and direct experiences, the level of self-identification with the experience was hypothesized to affect self-efficacy and its relationship with direct experiences. To test this, vicarious experiences with two distinct levels of self-identification were compared in a between-subjects experiment (n=60). After being exposed to a vicarious experience of giving lectures on elementary arithmetic in front of a virtual audience with either a high or low level of self-identification with the public speaker, participants from both conditions actively gave another lecture. The results revealed that self-identification affected people’s self-efficacy after vicarious experience. They further revealed that self-identification is a moderator of (1) the correlation between perceived performance and self-efficacy, (2) the correlation between self-efficacy measured after the vicarious and the follow-up direct experience; and (3) the correlation between the sense of presence reported in the vicarious and in the follow-up direct experience. We anticipate that the first-person-perspective experiences with high-level of self-identification have the potential to be beneficial for training where changing people’s self-efficacy is desirable.

Currently, expressive virtual humans are used in psychological research, training, and psychotherapy. However, the behavior of these virtual humans is usually scripted and therefore cannot be modified freely at runtime. To address this, we created a virtual audience with parameterized behavioral styles. This paper presents a parameterized audience model based on probabilistic models abstracted from the observation of real human audiences (n = 16). The audience's behavioral style is controlled by model parameters that define virtual humans' moods, attitudes, and personalities. Employing these parameters as predictors, the audience model significantly predicts audience behavior. To investigate if people can recognize the designed behavioral styles generated by this model, 12 audience styles were evaluated by two groups of participants. One group (n = 22) was asked to describe the virtual audience freely, and the other group (n = 22) was asked to rate the audiences on eight dimensions. The results indicated that people could recognize different audience attitudes and even perceive the different degrees of certain audience attitudes. In conclusion, the audience model can generate expressive behavior to show different attitudes by modulating model parameters.