Browsing by Subject "Scalability"

Biomedical time series contain rich information about human systems, however, effective algorithms for analyzing long-term physiological time series have not yet been developed because of the huge volume size, high dimensionality and large noise nature of the data. Motivated by such challenging task, we proposed a novel spectral embedding algorithm, which we coined Robust and Scalable Embedding via Landmark Diffusion (ROSELAND). The solution is a generic and not limited to analyze physiological waveforms. In short, we measure the affinity between two points via a set of landmarks, which is composed of a small number of points, and ``diffuse'' on the dataset via the landmark set to achieve a spectral embedding. The algorithm is applied to study the arterial blood pressure waveform dynamics during a liver transplant operation lasting for 12 hours long. In addition, we show that Roseland is not only numerically scalable, but also preserves the geometric properties via its diffusion nature under the manifold setup; that is, we theoretically explore the asymptotical behavior of Roseland under the manifold setup, and provide a L-infinity spectral convergence with a rate. Moreover, we offer a high dimensional noise analysis with the help of Gaussian approximation, and show that Roseland is robust to noise.

The correctness of a cache coherence protocol is crucial to the system since a subtle bug in the protocol may lead to disastrous consequences. However, the verification of a cache coherence protocol is never an easy task due to the complexity of the protocol. Moreover, as more and more cores are compressed into a single chip, there is an urge for the cache coherence protocol to have higher performance, lower power consumption, and less storage overhead. People perform various optimizations to meet these goals, which unfortunately, further exacerbate the verification problem. The current situation is that there are no efficient and universal methods for verifying a realistic cache coherence protocol for a many-core system.

We, as architects, believe that we can alleviate the verification problem by changing the traditional design paradigm. We suggest taking verifiability as a first-class design constraint, just as we do with other traditional metrics, such as performance, power consumption, and area overhead. To do this, we need to incorporate verification effort in the early design stage of a cache coherence protocol and make wise design decisions regarding the verifiability. Such a protocol will be amenable to verification and easier to be verified in a later stage. Specifically, we propose two methods in this thesis for designing scalably verifiable cache coherence protocols.

The first method is Fractal Coherence, targeting verifiable hierarchical protocols. Fractal Coherence leverages the fractal idea to design a cache coherence protocol. The self-similarity of the fractal enables the inductive verification of the protocol. Such a verification process is independent of the number of nodes and thus is scalable. We also design example protocols to show that Fractal Coherence protocols can attain comparable performance compared to a traditional snooping or directory protocol.

As a system scales hierarchically, Fractal Coherence can perfectly solve the verification problem of the implemented cache coherence protocol. However, Fractal Coherence cannot help if the system scales horizontally. Therefore, we propose the second method, PVCoherence, targeting verifiable flat protocols. PVCoherence is based on parametric verification, a widely used method for verifying the coherence of a flat protocol with infinite number of nodes. PVCoherence captures the fundamental requirements and limitations of parametric verification and proposes a set of guidelines for designing cache coherence protocols that are compatible with parametric verification. As long as designers follow these guidelines, their protocols can be easily verified.

We further show that Fractal Coherence and PVCoherence can also facilitate the verification of memory consistency, another extremely challenging problem. One piece of previous work proves that the verification of memory consistency can be decomposed into three steps. The most complex and non-scalable step is the verification of the cache coherence protocol. If we design the protocol following the design methodology of Fractal Coherence or PVCoherence, we can easily verify the cache coherence protocol and overcome the biggest obstacle in the verification of memory consistency.

As system expands and cache coherence protocols get more complex, the verification problem of the protocol becomes more prominent. We believe it is time to reconsider the traditional design flow in which verification is totally separated from the design stage. We show that by incorporating the verifiability in the early design stage and designing protocols to be scalably verifiable in the first place, we can greatly reduce the burden of verification. Meanwhile, we perform various experiments and show that we do not lose benefits in performance as well as in other metrics when we obtain the correctness guarantee.