JOYCE Core Algorithm

Cost Model

Due to previous model in [[JOYCE Project]] and [[JOYCE Paper Draft]].

We have some basic operators on statevector:

1. \(b_{CG}\), statevec swap (swap data for GPU device), depends on bandwidth between CPU - GPU
2. \(b_{GG}\), statevec comm (read data frem another device), depends on peer-to-peer bandwidth GPU - GPU
3. \(b_{CS}\), statevec offload, CPU - SSD
4. \(t_e\), operator execution, depends on FLOPS for GPU

\[ F(x_0, x_1, x_2) \] F: return value -> time cost for a gate execution x_0: bit mapping (qubit --> bit index in state vector) x_1: circuit info (matrix + target bit) x_2: cluster communication topology, piperadius

In user API point of view, we have:

1. Execute gate
2. Bit swap

Bit position

|<-SSD(S)->|<-DRAM(D)->|<-Multi GPU(M)->|<-GPU Local(L)->|

Execute gate

target bit on GPU Local Range

rounds = num_blocks / num_GPU Exec_Cost(GPU Local) = rounds * (t_e + state_on_gpu / b_CG)

target bit on GPU multiple Range

rounds = num_blocks / num_GPU Exec_Cost(GPU Comm) = rounds * (max(t_e, state_on_gpu / b_GG) + constant) ==(if pipeline)== Exec_Cost(GPU Comm) = rounds * (t_e + state_on_gpu / b_GG) ==(if no pipeline)==

target bit on Dram --> target bit on GPU multiple range

rounds = num_blocks / num_GPU Exec_Cost(DRAM) = Exec_Cost(GPU Comm) + state_on_gpu / b_CG

Bit swap

L, L

rounds = num_blocks / num_GPU Swap_Cost(L, L) = rounds * t_e

--> Discussion on a table:

9 conditions

Our Target: Bit swap can change locality, change bits on (S), D, M and L target function: total cost reduce to lowest