GPU Performance Engineer Test

The GPU Performance Engineer Test assesses a candidate's expertise in GPU programming, with a focus on CUDA and PyTorch. It uses scenario-based MCQs and coding questions to evaluate understanding of GPU architecture, optimization techniques, and memory management. The test is designed for roles requiring proficiency in parallel computing and performance tuning for high-performance applications.

Covered skills:

GPU architecture
CUDA programming
PyTorch frameworks
Parallel computing concepts
Memory management in GPUs
Optimization techniques
Deep learning acceleration
Performance tuning
Profiling GPU applications
Multi-GPU programming
Kernel design

Test Duration

~ 35 mins

Difficulty Level

Moderate

Questions

5 GPU Programming MCQs
5 PyTorch MCQs
5 CUDA MCQs

Availability

Available as custom test

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About the GPU Performance Engineer Assessment Test

The GPU Performance Engineer Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

Demonstrate a thorough understanding of GPU architecture and its impact on performance.
Write efficient CUDA kernels to leverage parallel computing capabilities.
Utilize PyTorch frameworks to develop and optimize deep learning models.
Implement effective memory management strategies to optimize GPU performance.
Apply advanced optimization techniques for enhanced computational efficiency.
Accelerate deep learning workloads using state-of-the-art practices.
Perform performance tuning to ensure applications run at maximum efficiency.
Develop profiling strategies to analyze and improve GPU application performance.
Design kernels that utilize the full potential of GPU hardware.
Handle multi-GPU programming challenges efficiently to scale up computations.
Integrate parallel computing concepts into GPU programming for optimized performance.
Show proficiency in adaptive techniques in varying computational loads.

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Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

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Why you should use Pre-employment GPU Performance Engineer Test?

The GPU Performance Engineer Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

Understanding basic GPU architecture and components.
Writing simple CUDA kernels for parallel tasks.
Implementing basic operations with PyTorch tensors.
Optimizing memory usage in GPU programs.
Applying fundamental parallel computing concepts.
Debugging CUDA kernels for logical errors.
Profiling GPU applications for bottleneck analysis.
Understanding PyTorch's automatic differentiation.
Performing simple matrix operations on GPU.
Managing memory transfers between CPU and GPU.

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the GPU Performance Engineer Test?

GPU Architecture: GPU architecture refers to the design and organization of graphics processing units, including their cores, memory, and data pathways. Understanding this architecture is vital for effectively harnessing a GPU's power and efficiency in computational tasks.

CUDA Programming: CUDA programming involves using NVIDIA's parallel computing platform and API model to execute code on GPUs. Mastery of CUDA enables developers to optimize computational tasks, making code execution more efficient on NVIDIA GPUs.

PyTorch Frameworks: PyTorch is an open-source machine learning library that offers dynamic computation graphs and deep learning capabilities. Skill in PyTorch allows engineers to prototype quickly and perform deep learning tasks effectively, crucial in GPU performance engineering.

Parallel Computing Concepts: Parallel computing involves dividing tasks into smaller parts that can run concurrently on multiple processors. This understanding is critical for maximizing performance in GPU computations, as GPUs are inherently parallel devices.

Memory Management in GPUs: Memory management in GPUs concerns the efficient use of memory resources to ensure fast and reliable data access during computations. Proper memory management is crucial for minimizing latency and maximizing throughput in GPU tasks.

Optimization Techniques: Optimization techniques in GPU programming involve enhancing performance through efficient code execution and resource utilization. Such skills allow engineers to reduce execution time and improve overall system performance.

Deep Learning Acceleration: Deep learning acceleration involves using GPUs to speed up training and inference of neural networks. With the growing demand for real-time data processing, this skill ensures rapid model deployment and scalability.

Performance Tuning: Performance tuning is the process of adjusting and improving system parameters to enhance efficiency. For GPUs, it involves optimizing code and configuring hardware settings to achieve optimal performance.

Profiling GPU Applications: Profiling involves analyzing and identifying performance bottlenecks in GPU applications. This skill helps in diagnosing issues and formulating strategies to boost application performance effectively.

Multi-GPU Programming: Multi-GPU programming is the technique of leveraging multiple GPUs to perform parallel processing tasks. It allows for scaling up computational workloads, which is fundamental in large-scale data processing and modeling tasks.

Kernel Design: Kernel design refers to the creation of small computing units that execute on the GPU. Efficient kernel design is essential for maximizing parallel execution and resource utilization on the GPU.

Full list of covered topics

The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for GPU Performance Engineer Test to be based on.

GPU Cores

CUDA Threads

Warp Scheduling

Tensor Cores

Global Memory

Shared Memory

Constant Memory

CUDA Streams

Dynamic Parallelism

Thrust Library

CuDNN Library

Memory Coalescing

Bank Conflict

Stream Synchronization

Kernel Launch

Device Query

Atomic Operations

Loop Unrolling

Error Handling

Compute Capability

Volta Architecture

Ampere Architecture

CUDA Toolkit

PyTorch Tensors

Model Serialization

Autograd

Loss Functions

Batch Processing

CUDA Graphs

Event Timing

SM Resources

NVProf Tool

NSight Analysis

CUDA Compiler

Kernel Fusion

RAPIDS AI

Grid Stride

Unified Memory

CUDA Aware MPI

CUDA Extensions

GPGPU Concepts

CUDA Runtime API

Deep Learning Models

Sparse Tensors

Broadcasting

Data Loaders

PyTorch Autograd

CUDA Debugging

Profiling TensorFlow

CudaMemcpyAsync

Kernel Optimization

What roles can I use the GPU Performance Engineer Test for?

GPU Performance Engineer
Machine Learning Engineer
AI Researcher
Data Scientist
Software Engineer
High Performance Computing Specialist
Deep Learning Engineer
Compute Programmer
Parallel Computing Developer
Graphics Programmer

How is the GPU Performance Engineer Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

Designing complex CUDA kernels for specific tasks.
Leveraging advanced PyTorch library features.
Optimizing parallel computations for performance gain.
Implementing multi-GPU processing techniques.
Applying sophisticated GPU memory management approaches.
Tuning GPU applications for maximum performance.
Accurate profiling of multi-threaded GPU applications.
Integrating CUDA and PyTorch for custom models.
Utilizing advanced kernel design strategies.
Employing deep learning acceleration techniques on GPUs.

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Have questions about the GPU Performance Engineer Hiring Test?

What is the GPU Performance Engineer Test?

The GPU Performance Engineer Test is designed to evaluate candidates' skills in GPU architecture, CUDA programming, PyTorch frameworks, and more. It is used by recruiters to identify candidates with strong GPU-focused software development abilities, making it easier to refine the hiring process.

Can I combine the GPU Performance Engineer Test with TensorFlow questions?

Yes, recruiters can request a customized test that includes skills from multiple areas. For a test that assesses TensorFlow skills, please check out our TensorFlow Test.

What topics are evaluated in the GPU Performance Engineer Test?

This test assesses proficiency in GPU architecture, CUDA programming, PyTorch frameworks, parallel computing concepts, GPU memory management, optimization techniques, deep learning acceleration, and more.

How to use the GPU Performance Engineer Test in my hiring process?

Use this test as an early-stage assessment to screen candidates by incorporating it in your job postings or direct email invitations. This approach replaces traditional resume screening for better candidate selection.

Can I test GPU programming and PyTorch together in a test?

Yes, testing GPU programming skills along with PyTorch is recommended for a comprehensive evaluation of candidates. Explore our PyTorch Test for more information.

What are the main AI and Machine Learning Tests?

Some key tests include the Deep Learning Test, Machine Learning Test, and AI Product Manager Test.

Can I combine multiple skills into one custom assessment?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

Hidden AI Tools Detection with Honestly
Non-googleable questions
IP proctoring
Screen proctoring
Web proctoring
Webcam proctoring
Plagiarism detection
Secure browser
Copy paste protection

Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

Here is a quick guide on how to request a custom assessment on Adaface.

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Along with scorecards that report the performance of the candidate in detail, you also receive a comparative analysis against the company average and industry standards.

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