Compute Resources

Compute resources encompass various types of hardware that perform computational tasks, including CPUs, GPUs, vector processors/cards, and QPUs. Each of these resources has unique characteristics and is suited to different types of computations & calculations.

Also presented here is a brief video tour showing how to access a detailed section for resources being used in the Qniverse application for job simulations and results.

 

 

Central Processing Unit (CPU)

 

 

  • Function: The primary processor in a computer, is responsible for executing general-purpose instructions and managing system operations.
  • Architecture: Composed of a few cores optimized for single-threaded performance and executing a wide range of instructions.
  • Use Cases: General computing tasks, including running operating systems, and applications, and managing input/output operations.
  • Performance: Typically measured in GHz, number of cores, and instruction per clock (IPC).

 

Graphics Processing Unit (GPU)

 

  • Function: Specialized processor designed to accelerate graphics rendering and parallel processing tasks.
  • Architecture: Contains thousands of smaller, efficient cores optimized for handling multiple tasks simultaneously.
  • Use Cases: Graphics rendering, video processing, machine learning, scientific simulations, and other parallelizable tasks.
  • Performance: Measured in teraflops (TFLOPS) for floating-point operations and the number of CUDA cores (for NVIDIA GPUs) or stream processors (for AMD GPUs).

The Aer_cuStateVector is a state vector simulator backend provided by IBM’s Qiskit Aer library. It is optimized to run on NVIDIA GPUs using cuStateVec, part of NVIDIA’s cuQuantum library. This backend harnesses the massive parallelism and computational power of GPUs to simulate quantum circuits much faster than traditional CPU-based simulators.

Key Features of Aer_cuStateVector:

  • GPU Acceleration: Utilizes NVIDIA GPUs to perform state vector simulations, significantly speeding up the process compared to CPU-based simulations.
  • cuStateVec Library: Part of NVIDIA’s cuQuantum framework, designed to optimize and accelerate quantum circuit simulations on GPUs.
  • State Vector Simulation: Efficiently handles the state vector representation of quantum circuits, allowing for high-performance simulation of large quantum systems.

Advantages:

Speed: Dramatically faster simulation times for large quantum circuits due to the parallel processing capabilities of GPUs.

Scalability: Can handle larger quantum circuits and higher qubit counts that are infeasible with CPU-only simulators.

Efficiency: Optimized use of GPU resources ensures efficient computation and resource utilization.

 

Vector Processors/Cards

 

  • Function: Processors designed to perform the same operation on multiple data points simultaneously, known as Single Instruction, Multiple Data (SIMD).
  • Architecture: Features vector registers and functional units that can perform operations on entire vectors in one instruction cycle.
  • Use Cases: High-performance computing (HPC) tasks, scientific computations, data analysis, and real-time signal processing.
  • Performance: Evaluated based on the width of vector units (e.g., 128-bit, 256-bit, 512-bit) and the ability to handle large vectors efficiently.

 

Quantum Processing Unit (QPU)

 

  • Function: Specialized processor that performs computations based on the principles of quantum mechanics.
  • Architecture: Utilizes qubits instead of bits, leveraging superposition, entanglement, and quantum gates to perform calculations.
  • Use Cases: Solving complex optimization problems, cryptography, drug discovery, material science, and other problems that benefit from quantum parallelism.
  • Performance: Measured in terms of the number of qubits, coherence time, gate fidelity, and quantum volume.

 

Comparative Overview

 

  • CPUs: Versatile and good for general-purpose computing but limited in parallel processing capabilities compared to GPUs.
  • GPUs: Excel at parallel processing, making them ideal for tasks that can be divided into smaller, concurrent operations.
  • Vector Processors: Optimize specific computations by processing multiple data points simultaneously, bridging the gap between general-purpose CPUs and highly parallel GPUs.
  • QPUs: Still in the experimental stage for most practical applications but offer the potential for exponential speedups in specific computational problems.

 

Integration in Computing

 

Modern computing systems often integrate multiple types of computing resources to leverage their respective strengths:

  • Hybrid Systems: Combining CPUs and GPUs in a single system for tasks like machine learning, where the CPU handles general control and logic while the GPU accelerates data processing.
  • High-Performance Computing (HPC): Utilizing vector processors and GPUs in supercomputers to achieve massive parallelism for scientific simulations and data analysis.
  • Quantum-Classical Hybrid Computing: Using QPUs in conjunction with classical CPUs/GPUs to solve parts of a problem more efficiently than either could alone, particularly in research and experimental setups.

 

Available platforms on Qniverse 

 

 

CPUs, GPU, Vector Processors and QPUs

 

Simulators on CPUs:

  • QASM_simulator
  • Statevector_simulator
  • Aer_simulator_density_matrix
  • Cirq Density Matrix Simulator

 

GPU:

  •  Aer_cuStateVector:

QPUs:

  • ibm_brisbane
  • ibm_kyoto
  • ibm_osaka
  • ibm_sherbrooke

HPC(High Performance Computers):

Param Utkarsh