Data races are one of the main culprits behind the complexity of multithreaded programming. Existing data race detectors require large amounts of metadata for each program variable to perform their analyses. The SLIMFAST system exploits the insight that there is a large amount of redundancy in this metadata: many program variables often have identical metadata state. By sharing metadata across variables, a large reduction in space usage can be realized. SLIMFAST uses immutable metadata to safely support metadata sharing across threads while also accelerating concurrency control. SLIMFAST's lossless metadata compression achieves these benefits while preserving soundness and completeness. Across a range of benchmarks from Java Grande, DaCapo, NAS Parallel Benchmarks and Oracle's BerkeleyDB Java Edition, SLIMFAST is able to reduce memory consumption by 1.83x on average, and up to 4.90x for some benchmarks, compared to the state-of-the-art FASTTRACK system. By improving cache locality and simplifying concurrency control, SLIMFAST also accelerates data race detection by 1.40x on average, and up to 8.8x for some benchmarks, compared to FASTTRACK.

As GPUs have become an integral part of nearly every pro- cessor, GPU programming has become increasingly popular. GPU programming requires a combination of extreme levels of parallelism and low-level programming, making it easy for concurrency bugs such as data races to arise. These con- currency bugs can be extremely subtle and di cult to debug due to the massive numbers of threads running concurrently on a modern GPU. While some tools exist to detect data races in GPU pro- grams, they are often prohibitively slow or focused only on a small class of data races in shared memory. Compared to prior work, our race detector, CURD, can detect data races precisely on both shared and global memory, selects an appropriate race detection algorithm based on the synchronization used in a program, and utilizes efficient compiler instrumentation to reduce performance overheads. Across 53 benchmarks, we find that using CURD incurs an aver- age slowdown of just 2.88x over native execution. CURD is 2.1x faster than Nvidia’s CUDA-Racecheck race detector, de- spite detecting a much broader class of races. CURD finds 35 races across our benchmarks, including bugs in established benchmark suites and in sample programs from Nvidia.

Writing race-free concurrent code is notoriously difficult, and data races can result in bugs that are difficult to isolate and reproduce. Dynamic race detection can catch data races that cannot (easily) be detected statically. One approach to dynamic race detection is to instrument the potentially racy code with operations that store and compare metadata, where the metadata implements some known race detection algorithm (e.g. vector-clock race detection). In this paper, we describe the process of formally verifying several algorithms for dynamic race detection. We then turn to implementations, laying out an instrumentation pass for race detection in a simple language and presenting a mechanized formal proof of its correctness: all races in a program will be caught by the instrumentation, and all races detected by the instrumentation are possible in the original program.

Data race detection is a useful dynamic analysis for multithreaded programs that is a key building block in record-and-replay, enforcing strong consistency models, and detecting concurrency bugs. Existing software race detectors are precise but slow, and hardware support for precise data race detection relies on assumptions like type safety that many programs violate in practice.

GPU programming models enable and encourage massively parallel programming with over a million threads, requiring extreme parallelism to achieve good performance. Massive parallelism brings significant correctness challenges by increasing the possibility for bugs as the number of thread interleavings balloons. Conventional dynamic safety analyses struggle to run at this scale.

Mobility model plays a crucial part in wireless network simulation or mathematical analysis, especially for human mobility because real mobility network constructed by human is hard to trace, measure or apply to different scenarios. For delay-tolerant networks, mobility model also affects the frequency and duration of opportunities for data transfer between nodes, which is a key factor of network performance, so whether we can choose a model according to realistic scenes is important for the accuracy and practicability of simulation. In this paper, we propose a new mobility model of students' daily activities based on the analysis and conclusion of students' habits and customs in campus environments, which is able to produce inter-contact time, contact time distribution and other contact information. We validate the movement model by using the ONE simulator comparing with other movement models and tracing data from real-world measurement experiments. Results show that SAME is consistent with actual student behavior characteristics.

In this paper, we focus on the capacity sensitivity of the cooperative MIMO relay network over spatially correlated channels. We study and compare the capacity loss of MMSE, MF, SVD, AF and ZF relaying schemes over Rayleigh flat fading channels where antennas of the source or destination terminal are correlated. Simulation results show that the MF relaying scheme is most sensitive for all existing relaying schemes. In order to improve the performance of capacity sensitivity for the MF relaying scheme, this paper proposes a modified MF relaying scheme, which is analogous to the MF relaying scheme but has much less capacity sensitivity.