The growing need for continuous processing capabilities has led to the development of multicore systems with a complex cache hierarchy. Such multicore systems are generally designed for improving the performance in average case, while hard real-time systems must consider worst-case scenarios. An open challenge is therefore to efficiently schedule hard real-time tasks on a multicore architecture. In this work, we propose a mathematical formulation for computing a static scheduling that minimize L1 data cache misses between hard real-time tasks on a multicore architecture using communication affinities.
@cite_1 focuses on the memory-to- @math traffic in the cache hierarchy of soft real-time systems. They propose a two steps method to discourage the co-scheduling of the tasks generating such traffic. First, the tasks that may induce significant memory-to- @math traffic are gathered into groups. Then at runtime, they use a scheduling policy that reduces concurrency within groups. @cite_2 also proposes several global multi-core scheduling strategies for soft real-time systems to minimize the @math cache trashing. Co-scheduling of the tasks of a same group is used to optimize the efficient use of the @math shared cache. Task promotion is another example of a studied scheduling policy. When considering hard real-time systems, to the best of our knowledge we are only aware of @cite_6 . Cache-partitioning techniques are used to avoid interferences, at the @math cache level, between the tasks that are running simultaneously. In addition to regular temporal constraints used within a schedulability test, cache constraints due to cache-partitionning are added and steer the computation of the scheduling. They propose a linear programming formulation to solve this problem and an approximation of this formulation for larger task sets.
@cite_1 @cite_6 @cite_2
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The major obstacle to use multicores for real-time applications is that we may not predict and provide any guarantee on real-time properties of embedded software on such platforms; the way of handling the on-chip shared resources such as L2 cache may have a significant impact on the timing predictability. In this paper, we propose to use cache space isolation techniques to avoid cache contention for hard real-time tasks running on multicores with shared caches. We present a scheduling strategy for real-time tasks with both timing and cache space constraints, which allows each task to use a fixed number of cache partitions, and makes sure that at any time a cache partition is occupied by at most one running task. In this way, the cache spaces of tasks are isolated at run-time. As technical contributions, we have developed a sufficient schedulability test for non-preemptive fixed-priority scheduling for multicores with shared L2 cache, encoded as a linear programming problem. To improve the scalability of the test, we then present our second schedulability test of quadratic complexity, which is an over approximation of the first test. To evaluate the performance and scalability of our techniques, we use randomly generated task sets. Our experiments show that the first test which employs an LP solver can easily handle task sets with thousands of tasks in minutes using a desktop computer. It is also shown that the second test is comparable with the first one in terms of precision, but scales much better due to its low complexity, and is therefore a good candidate for efficient schedulability tests in the design loop for embedded systems or as an on-line test for admission control. The execution of workloads such as web servers and database servers typically switches back and forth between different tasks such as user applications, system call handlers, and interrupt handlers. The combined size of the instruction footprints of such tasks typically exceeds that of the i-cache (16-32 KB). This causes a lot of i-cache misses and thereby reduces the application’s performance. Hence, we propose SchedTask, a hardware-assisted task scheduler that improves the performance of such workloads by executing tasks with similar instruction footprints on the same core. We start by decomposing the combined execution of the OS and the applications into sequences of instructions calledSuperFunctions. We propose a scheme to determine the amount of overlap between the instruction footprints of different SuperFunctions by using Bloom filters. We then use a hierarchical scheduler to execute SuperFunctions with similar instruction footprints on the same core. For a suite of 8 popular OS-intensive workloads, we report an increase in the application’s performance of up to 29 percentage points (mean: 11.4 percentage points) over state of the art scheduling techniques. CCS CONCEPTS • Software and its engineering @math Scheduling; Virtual memory; • Computer systems organization @math Multicore architectures; Cloud computing; Multitasked real-time systems often employ caches to boost performance. However the unpredictable dynamic behavior of caches makes schedulability analysis of such systems difficult. In particular, the effect of caches needs to be considered for estimating the inter-task interference. As the memory blocks of different tasks can map to the same cache blocks, preemption of a task may introduce additional cache misses. The time penalty introduced by these misses is called the cache-related preemption delay (CRPD). In this paper, we provide a program path analysis technique to estimate CRPD. Our technique performs path analysis of both the preempted and the preempting tasks. Furthermore, we improve the accuracy of the analysis by estimating the possible states of the entire cache at each possible preemption point rather than estimating the states of each cache block independently. To avoid incurring high space requirements, the cache states can be maintained symbolically as a binary decision diagram. Experimental results indicate that we obtain tight CRPD estimates for realistic benchmarks. We present new high performance numerical kernels combined with advanced optimization techniques that significantly increase the performance of parallel bidiagonal reduction. Our approach is based on developing efficient fine-grained computational tasks as well as reducing overheads associated with their high-level scheduling during the so-called bulge chasing procedure that is an essential phase of a scalable bidiagonalization procedure. In essence, we coalesce multiple tasks in a way that reduces the time needed to switch execution context between the scheduler and useful computational tasks. At the same time, we maintain the crucial information about the tasks and their data dependencies between the coalescing groups. This is the necessary condition to preserve numerical correctness of the computation. We show our annihilation strategy based on multiple applications of single orthogonal reflectors. Despite non-trivial characteristics in computational complexity and memory access patterns, our optimization approach smoothly applies to the annihilation scenario. The coalescing positively influences another equally important aspect of the bulge chasing stage: the memory reuse. For the tasks within the coalescing groups, the data is retained in high levels of the cache hierarchy and, as a consequence, operations that are normally memory-bound increase their ratio of computation to off-chip communication and become compute-bound which renders them amenable to efficient execution on multicore architectures. The performance for the new two-stage bidiagonal reduction is staggering. Our implementation results in up to 50-fold and 12-fold improvement ( 130 Gflop s) compared to the equivalent routines from LAPACK V3.2 and Intel MKL V10.3, respectively, on an eight socket hexa-core AMD Opteron multicore shared-memory system with a matrix size of 24000 &#x00D7; 24000. Last but not least, we provide a comprehensive study on the impact of the coalescing group size in terms of cache utilization and power consumption in the context of this new two-stage bidiagonal reduction.