Performance isolation is emerging as a requirement for High Performance Computing (HPC) applications, particularly as HPC architectures turn to in situ data processing and application composition techniques to increase system throughput. These approaches require the co-location of disparate workloads on the same compute node, each with different resource and runtime requirements. In this paper we claim that these workloads cannot be effectively managed by a single Operating System/Runtime (OS/R). Therefore, we present Pisces, a system software architecture that enables the co-existence of multiple independent and fully isolated OS/Rs, or enclaves, that can be customized to address the disparate requirements of next generation HPC workloads. Each enclave consists of a specialized lightweight OS cokernel and runtime, which is capable of independently managing partitions of dynamically assigned hardware resources. Contrary to other co-kernel approaches, in this work we consider performance isolation to be a primary requirement and present a novel co-kernel architecture to achieve this goal. We further present a set of design requirements necessary to ensure performance isolation, including: (1) elimination of cross OS dependencies, (2) internalized management of I/O, (3) limiting cross enclave communication to explicit shared memory channels, and (4) using virtualization techniques to provide missing OS features. The implementation of the Pisces co-kernel architecture is based on the Kitten Lightweight Kernel and Palacios Virtual Machine Monitor, two system software architectures designed specifically for HPC systems. Finally we will show that lightweight isolated co-kernels can provide better performance for HPC applications, and that isolated virtual machines are even capable of outperforming native environments in the presence of competing workloads.

With the growth of Infrastructure as a Service (IaaS) cloud providers, many have begun to seriously consider cloud services as a substrate for HPC applications. While the cloud promises many benefits for the HPC community, it currently does not come without drawbacks for application performance. These performance issues are generally the result of resource contention as multiple VMs compete for the same hardware. This contention culminates in cross VM interference whereby one VM is able to impact the performance of another. For HPC applications this interference can have a dramatic impact on scalability and performance. In order to fully support HPC applications in the cloud, services need to be available that prevent cross VM interference and isolate HPC workloads from other users. As a means to achieve this goal, we propose a dual stack approach to IaaS cloud services that utilizes multiple concurrent VMMs on each node capable of partitioning local resources in order to provide performance isolation. Each partition can then be managed by a specialized VMM that is designed specifically for either an HPC or commodity environment. In this paper we demonstrate the use of the Palacios VMM, a virtual machine monitor specifically designed for HPC, in concert with KVM to provide a partitioned cloud platform that is capable of hosting both commodity and HPC applications on a single node without interference. Furthermore, our results demonstrate that running KVM and Palacios in parallel allows an HPC application to achieve isolated and scalable performance while sharing hardware resources with commodity VMs.

When executing inside a virtual machine environment, OS level synchronization primitives are faced with significant challenges due to the scheduling behavior of the underlying virtual machine monitor. Operations that are ensured to last only a short amount of time on real hardware, are capable of taking considerably longer when running virtualized. This change in assumptions has significant impact when an OS is executing inside a critical region that is protected by a spinlock. The interaction between OS level spinlocks and VMM scheduling is known as the Lock Holder Preemption problem and has a significant impact on overall VM performance. However, with the use of ticket locks instead of generic spinlocks, virtual environments must also contend with waiters being preempted before they are able to acquire the lock. This has the effect of blocking access to a lock, even if the lock itself is available. We identify this scenario as the Lock Waiter Preemption problem. In order to solve both problems we introduce Preemptable Ticket spinlocks, a new locking primitive that is designed to enable a VM to always make forward progress by relaxing the ordering guarantees offered by ticket locks. We show that the use of Preemptable Ticket spinlocks improves VM performance by 5.32X on average, when running on a non paravirtual VMM, and by 7.91X when running on a VMM that supports a paravirtual locking interface, when executing a set of microbenchmarks as well as a realistic e-commerce benchmark.

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Virtual Machine based approaches to workload consolidation, as seen in IaaS cloud as well as datacenter platforms, have long had to contend with performance degradation caused by synchronization primitives inside the guest environments. These primitives can be affected by virtual CPU preemptions by the host scheduler that can introduce delays that are orders of magnitude longer than those primitives were designed for. While a significant amount of work has focused on the behavior of spinlock primitives as a source of these performance issues, spinlocks do not represent the entirety of synchronization mechanisms that are susceptible to scheduling issues when running in a virtualized environment. In this paper we address the virtualized performance issues introduced by TLB shootdown operations. Our profiling study, based on the PARSEC benchmark suite, has shown that up to 64% of a VM's CPU time can be spent on TLB shootdown operations under certain workloads. In order to address this problem, we present a paravirtual TLB shootdown scheme named Shoot4U. Shoot4U completely eliminates TLB shootdown preemptions by invalidating guest TLB entries from the VMM and allowing guest TLB shootdown operations to complete without waiting for remote virtual CPUs to be scheduled. Our performance evaluation using the PARSEC benchmark suite demonstrates that Shoot4U can reduce benchmark runtime by up to 85% compared an unmodified Linux kernel, and up to 44% over a state-of-the-art paravirtual TLB shootdown scheme.