Dynamic Thread Mapping for High-Performance,

Power-Efficient Heterogeneous Many-core Systems

Guangshuo LiuJinpyo Park

Diana MarculescuPresented By

Ravi Teja Arrabolu (vxa132930)

Introduction• The problem of dynamic thread mapping in heterogeneous

many-core systems is addressed via an efficient algorithm that maximizes performance under power constraints. Heterogeneous many-core systems are composed of

multiple core types with different power-performance characteristics.

• This paper proposes an iterative approach bounding the runtime as O(n2/m) , for mapping multi-threaded applications on n cores comprising of m core types

Heterogeneous Core Types

• A core type is defined by a tuple of micro-architecture features and associated nominal voltage/frequency.

• For example, cores that differ in architectural parameters, such as issue width, cache size, number of function units, etc., are considered as different core types.

• In addition, even if two cores are designed identically in terms of microarchitecture but associated with different nominal frequencies, they are considered as distinct core types.

General Notations• The throughput matrix is denoted by T R∈ n×m.Each element Tij represents the throughput ofthread i running on a core in type j. Throughput isdefined as the total number of instructions committed perunit of time.• The power matrix is denoted by P R∈ n×m.Each element Pij represents the total power consumptionfor thread i running on a core in type j.• The assignment matrix X {0, 1}∈ n×m represents theassignment of threads to each core type. Thread i ismapped to core type j if and only if Xij = 1.

Constraints• Objective function: Maximize performance defined by the total

throughput,maximize ∑i,j TijXij

• First, the power budget needs to be satisfied, ∑i,j i,j PijXij ≤ total TDP of cores.

• Second, a thread can be only mapped to a single core,∀i : ∑j Xij ≤ 1.

• The core count in each type is given, ∀j : ∑i Xij ≤ core countj .

• Finally, the mapping is a 0-1 assignment problem, and therefore∀i, j : ∑i,j Xij {0, 1}.∈

Experiment Setup• Sniper multi-core simulator was used to conduct the simulation

tests. • Sniper provides support of heterogeneous configurations and

dynamic thread migration.• It employs McPAT as the power estimation engine.• All the L1 and L2 caches are private to cores and there is no L3

cache.• For the cases in which the number of cores of each type is

identical, the network hierarchy is configured as a mesh of tiles.• If the core count is different in each type, we will have some

tiles with fewer core types.

MAXIMIZATION-THEN-SWAPPING (MTS)

IMPLEMENTATION

• Maximization : The heuristic assigns the threads having highest throughput and maps them in descending order of throughput to the available cores.• Swapping : The mapped threads are checked if they satisfy the power budget at the allotted cores, else they are mapped to appropriate downward/upward (type) cores. •P.S : Once we have no more changes, the swaps are registered.

Predicted Result Vs.

Runtime Result-16 core

•Results show the prediction error is 7% for power and

14.3%For throughput.

•To validate the prediction model, workloads mixes of multithreaded benchmarks

randomly selected from PARSEC and SPLASH-2

benchmark suites were utilized.

Heterogeneous Cores Types Configurations

Throughput / runtime comparison with ILP solver (one thread per core)

- 16 Cores

Throughput / runtime comparison with ILP solver (one thread per core)

- 64 cores

Scalability• Number of Cores

are scaled from 0 to 1000 cores.

• Time for scaling number of cores >100 and < 200 is 1 ms approximately.

History Based MTS• To minimize migration cost, we further enable MTS

heuristic to take the original mapping (history) into account.

• history ratio is introduced, which ranges from 0 to 1• the number of threads that are considered in the

maximization phase is limited to n×(1−history ratio).• As history ratio increases, the migration cost can be

reduced since fewer threads will be migrated between the original mapping and the final one.

Throughput Migration Trade off

Limitations• Load Balancing, No Simultaneous Multi threading

The paper confines to single thread per core which limits task migration between cores, which implies that sharing the threads is not considered, resulting in some tasks may miss deadlines.

• Fairness The paper does not consider each process's priority and workloads, but assigns the process to the cores depending on their types, which can result in CPU utilization loss, also effecting the QoS of such real time applications.