ILP and TLP in Shared Memory Applications: A Limit Study
MetadataShow full item record
The work in this dissertation explores the limits of Chip-multiprocessors (CMPs) with respect to shared-memory, multi-threaded benchmarks, which will help aid in identifying microarchitectural bottlenecks. This, in turn, will lead to more efficient CMP design. In the first part we introduce DotSim, a trace-driven toolkit designed to explore the limits of instruction and thread-level scaling and identify microarchitectural bottlenecks in multi-threaded applications. DotSim constructs an instruction-level Data Flow Graph (DFG) from each thread in multi-threaded applications, adjusting for inter-thread dependencies. The DFGs dynamically change depending on the microarchitectural constraints applied. Exploiting these DFGs allows for the easy extraction of the performance upper bound. We perform a case study on modeling the upper-bound performance limits of a processor microarchitecture modeled off a AMD Opteron. In the second part, we conduct a limit study simultaneously analyzing the two dominant forms of parallelism exploited by modern computer architectures: Instruction Level Parallelism (ILP) and Thread Level Parallelism (TLP). This study gives insight into the upper bounds of performance that future architectures can achieve. Furthermore, it identifies the bottlenecks of emerging workloads. To the best of our knowledge, our work is the first study that combines the two forms of parallelism into one study with modern applications. We evaluate the PARSEC multithreaded benchmark suite using DotSim. We make several contributions describing the high-level behavior of next-generation applications. For example, we show that these applications contain up to a factor of 929X more ILP than what is currently being extracted from real machines. We then show the effects of breaking the application into increasing numbers of threads (exploiting TLP), instruction window size, realistic branch prediction, realistic memory latency, and thread dependencies on exploitable ILP. Our examination shows that theses benchmarks differ vastly from one another. As a result, we expect that no single, homogeneous, micro-architecture will work optimally for all, arguing for reconfigurable, heterogeneous designs. In the third part of this thesis, we use our novel simulator DotSim to study the benefits of prefetching shared memory within critical sections. In this chapter we calculate the upper bound of performance under our given constraints. Our intent is to provide motivation for new techniques to exploit the potential benefits of reducing latency of shared memory among threads. We conduct an idealized workload characterization study focusing on the data that is truly shared among threads, using a simplified memory model. We explore the degree of shared memory criticality, and characterize the benefits of being able to use latency reducing techniques to reduce execution time and increase ILP. We find that on average true sharing among benchmarks is quite low compared to overall memory accesses on the critical path and overall program. We also find that truly shared memory between threads does not affect the critical path for the majority of benchmarks, and when it does the impact is less than 1%. Therefore, we conclude that it is not worth exploring latency reducing techniques of truly shared memory within critical sections.
Fatehi, Ehsan (2015). ILP and TLP in Shared Memory Applications: A Limit Study. Doctoral dissertation, Texas A & M University. Available electronically from