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While High Performance Computing systems are increas-ingly based on heterogeneous cores, their effectiveness depends on howwell the scheduler can allocate workloads onto appropriate computing de-vices and how communication and computation can be overlapped. Withdifferent types of resources integrated into one system, the complexity ofthe scheduler correspondingly increases. Moreover, for applications withvarying problem sizes on different heterogeneous resources, the optimalscheduling approach may vary accordingly. We thus present PDAWL, anevent-driven profile-based Iterative Dynamic Adaptive Work-Load bal-ance scheduling approach to dynamically and adaptively adjust workloadto efficiently utilize heterogeneous resources. It combines online schedul-ing (DAWL), which can adaptively adjust workload based on availablereal time heterogeneous resources, with an offline machine learning (profile-based estimation model) which can build a device-specific communica-tion computation estimation model. Our scheduling approach is tested oncontrol-regular applications, Stencil kernel (based on a Jacobi Algorithm)and Sparse Matrix-Vector Multiplication (SpMV) in an event-driven run-time system. Experimental results show that PDAWL is either on-par orfar outperforms whichever yields the best results (CPU or GPU). 

While High Performance Computing systems are increasingly based on heterogeneous cores, their eectiveness depends on how well the scheduler can allocate workloads onto appropriate computing devices and how communication and computation can be overlapped. With dierent types of resources integrated into one system, the complexity of the scheduler correspondingly increases. Moreover, for applications with varying problem sizes on dierent heterogeneous resources, the optimal scheduling approach may vary accordingly. We thus present PDAWL, an event-driven prole-based Iterative Dynamic Adaptive Work-Load balance scheduling approach to dynamically and adaptively adjust workload to eciently utilize heterogeneous resources. It combines online scheduling (DAWL), which can adaptively adjust workload based on available real time heterogeneous resources, with an oine machine learning (prolebased estimation model) which can build a device-specic communication computation estimation model. Our scheduling approach is tested on control-regular applications, Stencil kernel (based on a Jacobi Algorithm) and Sparse Matrix-Vector Multiplication (SpMV) in an event-driven runtime system. Experimental results show that PDAWL is either on-par or far outperforms whichever yields the best results (CPU or GPU). 

Over the past decades, the major objectives of computer design have been to improve performance and to reduce cost, energy consumption, and size, while security has remained a secondary concern. Meanwhile, malicious attacks have rapidly grown as the number of Internet-connected devices, ranging from personal smart embedded systems to large cloud servers, have been increasing. Traditional antivirus software cannot keep up with the increasing incidence of these attacks, especially for exploits targeting hardware design vulnerabilities. For example, as DRAM process technology scales down, it becomes easier for DRAM cells to electrically interact with each other. For instance, in Rowhammer attacks, it is possible to corrupt data in nearby rows by reading the same row in DRAM. As Rowhammer exploits a computer hardware weakness, no software patch can completely fix the problem. Similarly, there is no efficient software mitigation to the recently reported attack Spectre. The attack exploits microarchitectural design vulnerabilities to leak protected data through side channels. In general, completely fixing hardware-level vulnerabilities would require a redesign of the hardware which cannot be backported. In this paper, we demonstrate that by monitoring deviations in microarchitectural events such as cache misses, branch mispredictions from existing CPU performance counters, hardware-level attacks such as Rowhammer and Spectre can be efficiently detected during runtime with promising accuracy and reasonable performance overhead using various machine learning classifiers. 

To improve processor performance, computer architects have adopted such acceleration techniques as speculative execution and caching. However, researchers have recently discovered that this approach implies inherent security flaws, as exploited by Meltdown and Spectre. Attacks targeting these vulnerabilities can leak protected data through side channels such as data cache timing by exploiting mis-speculated executions. The flaws can be catastrophic because they are fundamental and widespread and they affect many modern processors. Mitigating the effect of Meltdown is relatively straightforward in that it entails a software-based fix which has already been deployed by major OS vendors. However, to this day, there is no effective mitigation to Spectre. Fixing the problem may require a redesign of the architecture for conditional execution in future processors. In addition, a Spectre attack is hard to detect using traditional software-based antivirus techniques because it does not leave traces in traditional log files. In this paper, we proposed to monitor microarchitectural events such as cache misses, branch mispredictions from existing CPU performance counters to detect Spectre during attack runtime. Our detector was able to achieve 0% false negatives with less than 1% false positives using various machine learning classifiers with a reasonable performance overhead.