BarrierPoint: sampled simulation of multi-threaded applications

Sampling is a well-known technique to speed up architectural simulation of long-running workloads while maintaining accurate performance predictions. A number of sampling techniques have recently been developed that extend well- known single-threaded techniques to allow sampled simulation of multi-threaded applications. Unfortunately, prior work is limited to non-synchronizing applications (e.g., server throughput workloads); requires the functional simulation of the entire application using a detailed cache hierarchy which limits the overall simulation speedup potential; leads to different units of work across different processor architectures which complicates performance analysis; or, requires massive machine resources to achieve reasonable simulation speedups. In this work, we propose BarrierPoint, a sampling methodology to accelerate simulation by leveraging globally synchronizing barriers in multi-threaded applications. BarrierPoint collects microarchitecture-independent code and data signatures to determine the most representative inter-barrier regions, called barrierpoints. BarrierPoint estimates total application execution time (and other performance metrics of interest) through detailed simulation of these barrierpoints only, leading to substantial simulation speedups. Barrierpoints can be simulated in parallel, use fewer simulation resources, and define fixed units of work to be used in performance comparisons across processor architectures. Our evaluation of BarrierPoint using NPB and Parsec benchmarks reports average simulation speedups of 24.7x (and up to 866.6x) with an average simulation error of 0.9% and 2.9% at most. On average, BarrierPoint reduces the number of simulation machine resources needed by 78x

Exploiting semantic commutativity in hardware speculation

Hardware speculative execution schemes such as hardware transactional memory (HTM) enjoy low run-time overheads but suffer from limited concurrency because they rely on reads and writes to detect conflicts. By contrast, software speculation schemes can exploit semantic knowledge of concurrent operations to reduce conflicts. In particular, they often exploit that many operations on shared data, like insertions into sets, are semantically commutative: they produce semantically equivalent results when reordered. However, software techniques often incur unacceptable run-time overheads. To solve this dichotomy, we present COMMTM, an HTM that exploits semantic commutativity. CommTM extends the coherence protocol and conflict detection scheme to support user-defined commutative operations. Multiple cores can perform commutative operations to the same data concurrently and without conflicts. CommTM preserves transactional guarantees and can be applied to arbitrary HTMs. CommTM scales on many operations that serialize in conventional HTMs, like set insertions, reference counting, and top-K insertions, and retains the low overhead of HTMs. As a result, at 128 cores, CommTM outperforms a conventional eager-lazy HTM by up to 3.4 χ and reduces or eliminates aborts.National Science Foundation (U.S.) (Grant CAREER-1452994

The ZCache: Decoupling Ways and Associativity

Abstract—The ever-increasing importance of main memory latency and bandwidth is pushing CMPs towards caches with higher capacity and associativity. Associativity is typically im-proved by increasing the number of ways. This reduces conflict misses, but increases hit latency and energy, placing a stringent trade-off on cache design. We present the zcache, a cache design that allows much higher associativity than the number of physical ways (e.g. a 64-associative cache with 4 ways). The zcache draws on previous research on skew-associative caches and cuckoo hashing. Hits, the common case, require a single lookup, incurring the latency and energy costs of a cache with a very low number of ways. On a miss, additional tag lookups happen off the critical path, yielding an arbitrarily large number of replacement candidates for the incoming block. Unlike conventional designs, the zcache provides associativity by increasing the number of replacement candidates, but not the number of cache ways. To understand the implications of this approach, we develop a general analysis framework that allows to compare associativity across different cache designs (e.g. a set-associative cache and a zcache) by representing associativity as a probability distribution. We use this framework to show that for zcaches, associativity depends only on the number of replacement candidates, and is independent of other factors (such as the number of cache ways or the workload). We also show that, for the same number of replacement candidates, the associativity of a zcache is superior than that of a set-associative cache for most workloads. Finally, we perform detailed simulations of multithreaded and multiprogrammed workloads on a large-scale CMP with zcache as the last-level cache. We show that zcaches provide higher performance and better energy efficiency than conventional caches without incurring the overheads of designs with a large number of ways. I

Jigsaw: Scalable software-defined caches

Shared last-level caches, widely used in chip-multi-processors (CMPs), face two fundamental limitations. First, the latency and energy of shared caches degrade as the system scales up. Second, when multiple workloads share the CMP, they suffer from interference in shared cache accesses. Unfortunately, prior research addressing one issue either ignores or worsens the other: NUCA techniques reduce access latency but are prone to hotspots and interference, and cache partitioning techniques only provide isolation but do not reduce access latency.United States. Defense Advanced Research Projects Agency (DARPA PERFECT contract HR0011-13-2-0005)Quanta Computer (Firm

Exploiting commutativity to reduce the cost of updates to shared data in cache-coherent systems

We present Coup, a technique to lower the cost of updates to shared data in cache-coherent systems. Coup exploits the insight that many update operations, such as additions and bitwise logical operations, are commutative: they produce the same final result regardless of the order they are performed in. Coup allows multiple private caches to simultaneously hold update-only permission to the same cache line. Caches with update-only permission can locally buffer and coalesce updates to the line, but cannot satisfy read requests. Upon a read request, Coup reduces the partial updates buffered in private caches to produce the final value. Coup integrates seamlessly into existing coherence protocols, requires inexpensive hardware, and does not affect the memory consistency model. We apply Coup to speed up single-word updates to shared data. On a simulated 128-core, 8-socket system, Coup accelerates state-of-the-art implementations of update-heavy algorithms by up to 2.4×.Center for Future Architectures ResearchNational Science Foundation (U.S.) (CAREER-1452994)Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Grier Presidential Fellowship)Microelectronics Advanced Research CorporationUnited States. Defense Advanced Research Projects Agenc

Most Progress Made Algorithm: Combating Synchronization Induced Performance Loss on Salvaged Chip Multi-Processors

Recent increases in hard fault rates in modern chip multi-processors have led to a variety of approaches to try and save manufacturing yield. Among these are: fine-grain fault tolerance (such as error correction coding, redundant cache lines, and redundant functional units), and large-grain fault tolerance (such as disabling of faulty cores, adding extra cores, and core salvaging techniques). This paper considers the case of core salvaging techniques and the heterogeneous performance introduced when these techniques have some salvaged and some non-faulty cores. It proposes a hypervisor-based hardware thread scheduler, triggered by detection of spin locks and thread imbalance, that mitigates the loss of throughput resulting from this het- erogeneity. Specifically, a new algorithm, called Most ProgressMade algorithm, reduces the number of synchronization locks held on a salvaged core and balances the time each thread in an application spends running on that core. For some benchmarks, the results show as much as a 2.68x increase in performance over a salvaged chip multi-processor without this technique

Impact Of Thread Scheduling On Modern Gpus

The Graphics Processing Unit (GPU) has become a more important component in high-performance computing systems as it accelerates data and compute intensive applications significantly with less cost and power. The GPU achieves high performance by executing massive number of threads in parallel in a SPMD (Single Program Multiple Data) fashion. Threads are grouped into workgroups by programmer and workgroups are then assigned to each compute core on the GPU by hardware. Once assigned, a workgroup is further subgrouped into wavefronts of the fixed number of threads by hardware when executed in a SIMD (Single Instruction Multiple Data) fashion. In this thesis, we investigated the impact of thread (at workgroup and wavefront level) scheduling on overall hardware utilization and performance. We implement four different thread schedulers: Two-level wavefront scheduler, Lookahead wavefront scheduler and Two-level + Lookahead wavefront scheduler, and Block workgroup scheduler. We implement and test these schedulers on a cycle accurate detailed architectural simulator called Multi2Sim targeting AMD\u27s latest Graphics Core Next (GCN) architecture. Our extensive evaluation and analysis show that using some of these alternate mechanisms, cache hit rate is improved by an average of 30% compared to the baseline round-robin scheduler, thus drastically reducing the number of stalls caused by long latency memory operations. We also observe that some of these schedulers improve overall performance by an average of 17% compared to the baseline

Scaling Distributed Cache Hierarchies through Computation and Data Co-Scheduling

Cache hierarchies are increasingly non-uniform, so for systems to scale efficiently, data must be close to the threads that use it. Moreover, cache capacity is limited and contended among threads, introducing complex capacity/latency tradeoffs. Prior NUCA schemes have focused on managing data to reduce access latency, but have ignored thread placement; and applying prior NUMA thread placement schemes to NUCA is inefficient, as capacity, not bandwidth, is the main constraint. We present CDCS, a technique to jointly place threads and data in multicores with distributed shared caches. We develop novel monitoring hardware that enables fine-grained space allocation on large caches, and data movement support to allow frequent full-chip reconfigurations. On a 64-core system, CDCS outperforms an S-NUCA LLC by 46% on average (up to 76%) in weighted speedup and saves 36% of system energy. CDCS also outperforms state-of-the-art NUCA schemes under different thread scheduling policies.National Science Foundation (U.S.) (Grant CCF-1318384)Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Jacobs Presidential Fellowship)United States. Defense Advanced Research Projects Agency (PERFECT Contract HR0011-13-2-0005