Virtual Machine Support for Many-Core Architectures: Decoupling Abstract from Concrete Concurrency Models

The upcoming many-core architectures require software developers to exploit concurrency to utilize available computational power. Today's high-level language virtual machines (VMs), which are a cornerstone of software development, do not provide sufficient abstraction for concurrency concepts. We analyze concrete and abstract concurrency models and identify the challenges they impose for VMs. To provide sufficient concurrency support in VMs, we propose to integrate concurrency operations into VM instruction sets. Since there will always be VMs optimized for special purposes, our goal is to develop a methodology to design instruction sets with concurrency support. Therefore, we also propose a list of trade-offs that have to be investigated to advise the design of such instruction sets. As a first experiment, we implemented one instruction set extension for shared memory and one for non-shared memory concurrency. From our experimental results, we derived a list of requirements for a full-grown experimental environment for further research

Dynamic shared memory architecture, systems, and optimizations for high performance and secure virtualized cloud

Dynamic memory consolidation is an important enabler for high performance virtual machine (VM) execution in virtualized Cloud. Efficient just-in-time memory balancing requires three core capabilities: (i) Detecting memory pressure across VMs hosted on a physical machine; (ii) Allocation of memory to respective VMs; (iii) Enabling fast recovery upon making newly allocated memory available at the high pressure VMs. Although the Balloon driver technology facilitates the second task, it remains difficult to accurately predict the VM memory demands at affordable overhead, especially under unpredictable and changing workloads. Furthermore, no prior study analyzed the effect of slow response of VM execution to the newly available memory due to paging based application recovery. In this dissertation research, I have made four original contributions to dynamic shared memory management in terms of architecture, systems and optimizations for improving VM execution performance and security. First, we designed and developed MemPipe, a shared memory inter-VM communication channel for fast inter-VM network I/O. MemPipe increases the shared memory utilization by adaptively adjusting the shared memory size according to workloads demands. It also reduces the inter-VM network communication overhead by directly copying the packets from the sender VM's user space to the shared memory area. Second, we developed iBalloon, a light-weight and transparent prediction based facility to enable automated or semi-automated ballooning with more customizable, accurate, and efficient memory balancing policies among VMs. Third, we developed MemFlex, a novel shared memory swapping facility that can effectively utilizes host idle memory by a hybrid memory swap-out model and a fast swap-in optimization. Fourth, we introduced SecureStack, which is a kernel backed tool to prevent the sensitive data on the function stack from being illegally accessed by the untrusted functions. SecureStack introduces three procedures to protect, restore, and clear the stack in a reliable and low cost manner. It is highly transparent to the users and does not bring any new vulnerability to the existing system. The above research developments are packaged into MemLego, a new memory management framework for memory-centric computing in the big data era.Ph.D

Execution time supports for adaptive scientific algorithms on distributed memory machines

Optimizations are considered that are required for efficient execution of code segments that consists of loops over distributed data structures. The PARTI (Parallel Automated Runtime Toolkit at ICASE) execution time primitives are designed to carry out these optimizations and can be used to implement a wide range of scientific algorithms on distributed memory machines. These primitives allow the user to control array mappings in a way that gives an appearance of shared memory. Computations can be based on a global index set. Primitives are used to carry out gather and scatter operations on distributed arrays. Communications patterns are derived at runtime, and the appropriate send and receive messages are automatically generated