GPU-accelerated molecular dynamics (MD) simulations have enabled significant advances in modeling biomolecular systems and extended timescales. While system size, typically measured by atom count, is often used to estimate simulation performance, the impact of biophysical system composition remains underexplored. This study investigated GPU-accelerated simulation throughput using GROMACS on an NVIDIA Titan RTX GPU across eight atom count-controlled systems (approximately 82,000 atoms) with varying biophysical compositions. The benchmark set included water-only, protein-ligand, membrane-only, and membrane-protein systems, matched to the standard benchMEM system for controlled comparison. MD systems with high water and protein content consistently demonstrated superior GPU performance exceeding 30% compared to lipid-rich systems, despite equivalent atom counts. Membrane-protein systems exhibited modest improvements over membrane-only systems, likely due to more favorable domain decomposition properties. These findings provide quantitative insight into the influence of system composition on GPU performance and offer practical information for optimizing MD simulation systems in terms of biological complexity and computational efficiency.

Network middleboxes provide the first line of defense for enterprise networks. Many of them typically inspect packet payload to filter malicious attack patterns. However, the widespread use of end-to-end cryptographic protocols designed to promote security and privacy, either inhibits deep packet inspection in the network or forces enterprises to use solutions that are not secure. This article introduces a complete framework for building secure and practical network middleboxes, called EVE, which enables visibility over encrypted traffic. EVE securely processes encrypted traffic using a combination of hardware-based trusted execution and software security technology. For enhanced programmability and security, EVE provides a high-level programming interface based on the Rust language. The high-level APIs of EVE provide security and significantly ease the development effort by hiding the details of cryptographic operations, enclave processing, TCP reassembly, and out-of-band key sharing. Our evaluation shows EVE supports diverse use cases with multiple encryption protocols in a secure fashion while delivering high performance.

Minimizing response time of mobile applications is critical for user experience. Existing work predominantly focuses on reducing mobile Web latency, whereas users spend more time on native mobile apps than mobile Web. Similar to Web, mobile apps contain a chain of dependencies between successive requests. However, unlike Web acceleration where object dependencies can easily be identified by parsing Web documents, App acceleration is much more difficult because the dependency is encoded in the app binary.

A network middlebox benefits both users and network operators by offering a wide range of security-related in-network functions, such as web firewalls and intrusion detection systems (IDS). However, the wide usage of encryption protocol restricts functionalities of network middleboxes. This forces network operators and users to make a choice between end-to-end privacy and security. This paper presents SGX-Box, a secure middlebox system that enables visibility on encrypted traffic by leveraging Intel SGX technology. The entire process of SGX-Box ensures that the sensitive information, such as decrypted payloads and session keys, is securely protected within the SGX enclave. SGX-Box provides easy-to-use abstraction and a high-level programming language, called SB lang for handling encrypted traffic in middleboxes. It greatly enhances programmability by hiding details of the cryptographic operations and the implementation details in SGX enclave processing. We implement a proof-of-concept IDS using SB lang. Our preliminary evaluation shows that SGX-Box incurs acceptable performance overhead while it dramatically reduces middlebox developer's effort.

Network applications and protocols are increasingly adopting security and privacy features, as they are becoming one of the primary requirements. The wide-spread use of transport layer security (TLS) and the growing popularity of anonymity networks, such as Tor, exemplify this trend. Motivated by the recent movement towards commoditization of trusted execution environments (TEEs), this paper explores alternative design choices that application and protocol designers should consider. In particular, we explore the possibility of using Intel SGX to provide security and privacy in a wide range of network applications. We show that leveraging hardware protection of TEEs opens up new possibilities, often at the benefit of a much simplified application/protocol design. We demonstrate its practical implications by exploring the design space for SGX-enabled software-defined inter-domain routing, peer-to-peer anonymity networks (Tor), and middleboxes. Finally, we quantify the potential overheads of the SGX-enabled design by implementing it on top of OpenSGX, an open source SGX emulator.

Recent advent of manycore system increases needs for larger but faster memory hierarchy. Emerging next generation memories such as on-chip DRAM and nonvolatile memory (NVRAM) are promising candidates for replacement of DRAM-only main memory. Combined with the manycore trends, it gives an opportunity to rethink conventional resource management system with a memory hierarchy for a single cloud node. In an attempt to mitigate the energy and memory problems, we propose MN-MATE, an elastic resource management architecture for a single cloud node with manycores, on-chip DRAM, and large size of off-chip DRAM and NVRAM. In MN-MATE, the hypervisor places consolidated VMs and balances memory among them. Based on the monitored information about the allocated memory, a guest OS co-schedules tasks accessing different types of memory with complementary access intensity. Polymorphic management of DRAM hierarchy accelerates average memory access speed inside each guest OS. A guest OS reduces energy consumption with small performance loss based on the NVRAM-aware data placement policy and the hybrid page cache. A new lightweight kernel is developed to reduce the overhead from the guest OS for scientific applications. Experiment results show that our techniques in MN-MATE platform improve system performance and reduce energy consumption.

Main memory as a hybrid between DRAM and nonvolatile memory is rapidly considered as a basic building block of computing systems. Despite widely-performed researches no one can confirm whether hybrid memory is at its full performance in terms of energy consumption, time delay or both. The main problem is that evaluating their performance in comparison with the optimal performance is challenging since deriving the optimal value is NP-complete. In this paper, we design and implement an evaluation framework termed OPAMP, which calculates optimal performance of the hybrid memory environment. This system gathers workload, specification of DRAM and PRAM, and environmental parameters of the hybrid main memory. After that, it calculates the maximum performance under the corresponding conditions. We suggest the way of deriving the optimal value by profiling instead of page migration which is the mainstream of recent researches on hybrid main memory system. Also, proportion of DRAM's size to PRAM's and proportion of DRAM's usage space to PRAM's are impactive factors. While designing hybrid main memory, those two variables must be determined carefully and OPAMP gives the guideline to the researchers.