Introduction and Matrix Multiplication

Question 1

What is the recommended approach when parallelizing loops, specifically outer loops vs. inner loops?

Answer 1

The recommended approach is to parallelize outer loops rather than inner loops.

Question 2

What optimization technique can be applied to matrix multiplication to improve cache efficiency?

Answer 2

Tiling, or breaking matrices into smaller blocks, can significantly improve cache efficiency in matrix multiplication.

Question 3

How can you determine the appropriate tile size in tiling matrix multiplication?

Answer 3

Experimentation and testing with different tile sizes are crucial to finding the optimal value for the tile size.

Question 4

What role do cache misses play in the efficiency of matrix multiplication?

Answer 4

Cache misses can lead to slower performance. Tiling and optimizing for cache utilization can help minimize cache misses.

Question 5

In the context of vectorization, what is SIMD, and how does it relate to vector hardware?

Answer 5

SIMD stands for Single-Instruction stream, Multiple-Data. It is a type of parallelism where a single instruction operates on multiple data elements simultaneously. Vector hardware processes data in SIMD fashion.

Question 6

How can you enhance vectorization in code using compiler flags?

Answer 6

Compiler flags like AVX, AVX2, and fast math can enhance vectorization. Choosing the appropriate flags for the target architecture is crucial.

Question 7

What is the significance of the base case in the divide-and-conquer approach, and how does it impact performance?

Answer 7

The base case in divide and conquer determines when to switch to a standard algorithm. Setting a threshold for the base case helps control function call overhead, improving performance.

Question 8

What is the final performance achieved in matrix multiplication, and why might it not reach peak performance?

Answer 8

The final performance reached is 41% of peak, with a 50,000x speedup. It might not reach peak due to assumptions made in optimization or specific cases where other libraries excel.

Question 9

How does the Intel Math Kernel Library (MKL) compare to the optimized matrix multiplication discussed?

Answer 9

Intel MKL is professionally engineered and might outperform in scenarios where assumptions made in the optimization process do not hold. The discussed method excels in specific cases.

Question 10

What is the primary focus of this course regarding computing topics?

Answer 10

The course focuses on multicore computing, emphasizing mastery in multicore performance engineering. It does not cover GPUs, file systems, or network performance.

Question 11

How does tiling in matrix multiplication help improve cache utilization?

Answer 11

Tiling reduces the number of memory accesses by breaking the computation into smaller blocks, enhancing spatial locality and minimizing cache misses.

Question 12

In the context of vectorization, what is a SIMD lane, and why is it essential to maintain uniform operations within a lane?

Answer 12

A SIMD lane is a processing unit that performs the same operation on multiple data elements. It’s crucial to maintain uniform operations within a lane to leverage vector hardware efficiently.

Question 13

How does experimenting with different tile sizes contribute to optimizing matrix multiplication?

Answer 13

Experimentation helps find the optimal tile size for tiling matrix multiplication, balancing factors like cache efficiency and computational overhead.

Question 14

What challenges arise when optimizing code for vectorization, and how can the “fast math” flag address them?

Answer 14

Challenges include non-associativity of floating-point operations. The “fast math” flag allows the compiler to reorder operations for improved performance, but it might change numerical precision.

Question 15

Explain the significance of the threshold in the base case of divide-and-conquer approaches.

Answer 15

The threshold in the base case determines when to switch to a standard algorithm, balancing function call overhead. It is essential for controlling the recursion depth.

Question 16

Why might the performance improvement not be as dramatic in other scenarios compared to matrix multiplication?

Answer 16

Matrix multiplication is a particularly suitable example due to its inherent parallelism. Other algorithms or applications may not exhibit the same level of improvement with optimization techniques.

Question 17

What is the key takeaway from achieving 41% of peak performance in matrix multiplication?

Answer 17

The achieved performance, despite not reaching peak, represents a significant improvement, showcasing the effectiveness of optimization techniques.

Question 18

In multicore computing, why is it beneficial to focus on mastering the domain before expanding into other areas like GPUs or file systems?

Answer 18

Mastering multicore performance engineering provides a strong foundation, making it easier to excel in other computing domains. It’s a strategic approach to learning.

Question 19

What is the key insight for improving matrix multiplication performance?

Answer 19

The key insight is to use parallel processing, specifically parallelizing outer loops and optimizing cache usage.

Question 20

What is the impact of parallelizing loops on running times?

Answer 20

Parallelizing outer loops can lead to significant speedup, while parallelizing inner loops may have scheduling overhead issues.

Question 21

What is the rule of thumb for parallelizing loops?

Answer 21

Parallelize outer loops rather than inner loops for better performance.

Question 22

What optimization technique involves breaking matrices into smaller blocks?

Answer 22

Tiling or blocking involves breaking matrices into smaller blocks to improve cache utilization.

Question 23

How does tiling reduce memory accesses in matrix multiplication?

Answer 23

Tiling reduces memory accesses by computing a block of the matrix, requiring fewer reads and writes compared to computing a full row.

Question 24

What is the impact of tiling on performance in matrix multiplication?

Answer 24

Tiling can significantly improve performance, and tuning the tile size is crucial for optimal results.

Question 25

What are the three levels of caching in a processor?

Answer 25

L1-cache, L2-cache, and L3-cache.

Question 26

How can you achieve two-level tiling and what are the tuning parameters?

Answer 26

Achieve two-level tiling with tuning parameters ‘s’ and ‘t,’ representing block sizes. Experimentation is needed to find optimal values.

Question 27

What is the term for processing data in SIMD fashion using vector hardware?

Answer 27

SIMD stands for Single-Instruction stream, Multiple-Data. It involves processing data in vector units.

Question 28

What compiler flags can be used to enable vectorization?

Answer 28

Flags like -mavx, -mavx2, and -ffast-math can enable vectorization in compilers.

Question 29

What factor limits achieving peak performance in matrix multiplication?

Answer 29

Achieving peak performance is limited by factors such as assumptions about matrix size, and professionally engineered libraries like Intel MKL may outperform custom solutions in some cases.

Question 30

What domain does the lecture primarily focus on in terms of performance engineering?

Answer 30

The lecture primarily focuses on multicore computing, emphasizing mastering multicore performance engineering.

Question 31

What caution is given regarding the comparison of different CPUs based on clock speed?

Answer 31

Comparing CPUs based solely on clock speed may not accurately reflect their capabilities; factors like architecture and design are crucial.

Question 32

What foundation does mastering multicore performance engineering provide for engineers?

Answer 32

Mastering multicore performance engineering provides a foundation for excelling in other domains like GPUs, file systems, and network performance.

Question 33

What is clock speed, and how is it measured?

Answer 33

Clock speed is the number of cycles a CPU can execute per second, measured in Hertz (Hz).

Question 34

What is Hyper-Threading?

Answer 34

Hyper-Threading is a technology that enables a single physical processor core to execute multiple threads concurrently, improving overall CPU efficiency.

Question 35

How does Hyper-Threading work?

Answer 35

Hyper-Threading works by allowing the CPU core to work on more than one set of tasks simultaneously, sharing resources between multiple threads.

Question 36

What is the purpose of Hyper-Threading in terms of performance?

Answer 36

Hyper-Threading aims to increase CPU utilization and throughput by enabling the execution of multiple threads in parallel on a single core.

Question 37

What is a “logical processor” in the context of Hyper-Threading?

Answer 37

A logical processor is a virtualized execution unit created by Hyper-Threading, allowing the operating system to schedule tasks independently for each logical processor.

Question 38

Does Hyper-Threading double the number of physical cores in a CPU?

Answer 38

No, Hyper-Threading doesn’t double the physical cores. It creates additional logical processors to enhance parallelism without adding more physical cores.

Question 39

What are the potential benefits of Hyper-Threading?

Answer 39

Benefits include improved multitasking, better resource utilization, and increased throughput by leveraging parallelism in applications.

Question 40

Can all software take full advantage of Hyper-Threading?

Answer 40

Not all software can fully utilize Hyper-Threading. Applications must be designed or optimized for parallel execution to benefit from this technology.

Question 41

What is the impact of Hyper-Threading on single-threaded applications?

Answer 41

Hyper-Threading might not significantly benefit single-threaded applications, and in some cases, it could lead to performance degradation due to resource sharing.

Question 42

Are there situations where it’s better to disable Hyper-Threading?

Answer 42

Yes, in certain scenarios, like specific gaming situations or applications sensitive to thread contention, disabling Hyper-Threading might result in better performance.

Question 43

How can you check if Hyper-Threading is enabled on your system?

Answer 43

You can check system information or use utilities like Task Manager (Windows) or lscpu (Linux) to see the number of logical processors compared to physical cores.

Question 44

Does Hyper-Threading replace the need for physical cores?

Answer 44

No, Hyper-Threading complements physical cores but doesn’t replace them. Physical cores remain crucial for parallel processing, and the combination enhances overall system performance.

Question 45

What does “Percent of Peak” refer to in performance evaluation?

Answer 45

“Percent of Peak” is a metric indicating the ratio of a system’s achieved performance to its theoretical peak performance, often expressed as a percentage.

Question 46

Why is “Percent of Peak” important in performance engineering?

Answer 46

It provides insights into how efficiently a system is utilizing its resources compared to the maximum potential performance, helping identify bottlenecks and areas for improvement.

Question 47

How is “Percent of Peak” calculated?

Answer 47

The calculation involves dividing the actual performance achieved by the system by its theoretical peak performance, then multiplying by 100 to get the percentage.

Question 48

What factors can influence the “Percent of Peak” performance?

Answer 48

Factors include CPU architecture, clock speed, memory bandwidth, parallelism, code optimization, and the efficiency of utilizing hardware resources.

Question 49

What is a “Compiler” in programming?

Answer 49

A compiler is a software tool that translates the entire program’s source code into machine code or an intermediate code before execution. The resulting compiled code can be executed independently of the original source code.

Question 50

What is an “Interpreter” in programming?

Answer 50

An interpreter is a program that directly executes source code line by line without prior translation. It translates and executes the code simultaneously, interpreting each statement at runtime.

Question 51

What is the key difference between a “Compiler” and an “Interpreter”?

Answer 51

The main difference lies in the translation process. A compiler translates the entire source code before execution, producing an executable file. An interpreter translates and executes code line by line without generating a separate executable.

Question 52

Advantages of using a “Compiler” in programming?

Answer 52

Compilation usually results in faster execution as the entire code is translated upfront, and the compiled code can be distributed without revealing the source. Additionally, it may perform optimizations during compilation.

Question 53

Advantages of using an “Interpreter” in programming?

Answer 53

Interpreters are more flexible and provide dynamic execution, allowing immediate feedback during development. They are suitable for certain types of applications and support interactive debugging.

Question 54

Do compilers generate an intermediate code during the translation process?

Answer 54

Some compilers generate an intermediate code, which is an abstraction of the source code, before producing the final machine code or executable. This intermediate code aids in portability and optimization.

Question 55

Can an interpreter execute code written in high-level languages directly?

Answer 55

Yes, interpreters can execute code written in high-level languages directly, translating and executing each line on-the-fly without producing a separate compiled version.

Question 56

Which phase typically takes longer: compilation or interpretation?

Answer 56

Compilation generally takes longer because it involves translating the entire source code upfront. Interpreters provide immediate execution but may involve repeated translation for each run.

Question 57

Is Java compiled or interpreted?

Answer 57

Java uses a combination of compilation and interpretation. Java source code is initially compiled into an intermediate bytecode, and then the Java Virtual Machine (JVM) interprets and executes this bytecode.

Question 58

Are there programming languages that use both compilation and interpretation?

Answer 58

Yes, there are languages that use a combination of both techniques, known as “just-in-time compilation” (JIT). Examples include Java, C#, and Python (with certain implementations).

Question 59

Which approach, compilation, or interpretation, is more closely associated with static typing?

Answer 59

Compilation is often associated with statically-typed languages, where type checking is performed at compile-time. Interpreters may be more common in dynamically-typed languages, where type checking occurs at runtime.

Question 60

What is “Cache Locality” in computer systems?

Answer 60

Cache locality refers to the tendency of a program to access data that is stored close to other accessed data in the cache. It aims to maximize the use of cache memory by exploiting spatial and temporal locality.

Question 61

What is “Spatial Locality” in the context of cache?

Answer 61

Spatial locality refers to the tendency of a program to access memory locations that are close to each other. Utilizing spatial locality in cache design involves loading entire blocks of contiguous memory into the cache, as adjacent data is likely to be accessed soon.

Question 62

What is “Temporal Locality” in the context of cache?

Answer 62

Temporal locality refers to the likelihood of accessing the same memory locations repeatedly within a short period. Caching mechanisms take advantage of temporal locality by keeping recently accessed data in the cache, anticipating it will be needed again soon.

Question 63

How does “Cache Locality” contribute to improved performance?

Answer 63

Cache locality enhances performance by reducing the time it takes to retrieve data from memory. Spatial locality ensures that adjacent data is preloaded into the cache, and temporal locality ensures that frequently accessed data remains in the cache, minimizing memory access times.

Question 64

Why is “Cache Locality” important for program efficiency?

Answer 64

Efficient use of cache memory is crucial for program performance. Cache locality minimizes the delay caused by fetching data from main memory, allowing the CPU to access frequently used data quickly.

Question 65

What programming practices can improve “Cache Locality”?

Answer 65

Strategies like using arrays instead of linked lists, accessing elements in a sequential manner, and organizing data structures to enhance spatial and temporal locality can improve cache locality in programs.

Question 66

How do cache misses impact “Cache Locality”?

Answer 66

Cache misses occur when the required data is not present in the cache. Excessive cache misses can disrupt cache locality, leading to suboptimal performance. Optimizing algorithms and data access patterns helps reduce cache misses.

Question 67

In the context of cache design, what is “Cache Line” or “Cache Block”?

Answer 67

A cache line or cache block is the smallest unit of data that can be stored in the cache. It is typically several consecutive bytes, and when one memory location is accessed, an entire cache line is loaded into the cache.

Question 68

How does “Cache Associativity” relate to “Cache Locality”?

Answer 68

Cache associativity determines how multiple memory blocks can map to the same cache set. Higher associativity can improve cache locality as it allows more flexibility in storing related data in the same set, reducing conflicts.

Question 69

Give an example of a programming scenario that benefits from “Cache Locality.”

Answer 69

Iterating over elements of a contiguous array benefits from spatial locality, as adjacent array elements are loaded into the cache together, improving access times during the iteration.

Question 70

What are “Compiler Optimization Flags”?

Answer 70

Compiler optimization flags are directives provided to a compiler during the compilation of source code to instruct the compiler on specific optimizations to apply. These flags aim to enhance the performance, size, or debugging capabilities of the compiled code.

Question 71

Give examples of common “Compiler Optimization Flags.”

Answer 71

Examples include:
-O1: Basic optimization level.
-O2 or -O3: Higher optimization levels with increased aggressiveness.
-Os: Optimize for code size.
-ffast-math: Allows reordering of floating-point operations for speed.
-march=native: Generate code optimized for the host machine’s architecture.

Question 72

What does the flag -O2 or -O3 signify in compiler optimization?

Answer 72

The flags -O2 and -O3 represent higher levels of optimization in a compiler. They instruct the compiler to apply more aggressive optimizations, potentially resulting in faster and more efficient code. However, higher optimization levels may increase compilation time.

Question 73

How does the flag -Os differ from other optimization flags?

Answer 73

The -Os flag instructs the compiler to optimize for code size rather than execution speed. It aims to generate compact binaries, prioritizing reduced executable size over maximum performance.

Question 74

Explain the purpose of the -ffast-math optimization flag.

Answer 74

The -ffast-math flag allows the compiler to perform aggressive floating-point optimizations, including reordering operations for speed. It sacrifices strict adherence to floating-point standards for improved numerical performance.

Question 75

When would you use the -march=native flag in compiler optimization?

Answer 75

The -march=native flag directs the compiler to generate code optimized for the host machine’s architecture. It is used when maximum performance specific to the underlying hardware is desired.

Question 76

What considerations should be kept in mind when using optimization flags?

Answer 76

Considerations include:
Balancing between speed and code size based on application requirements.
Verifying that aggressive optimizations do not compromise program correctness.
Understanding that higher optimization levels may increase compilation time.

Question 77

How can compiler optimization flags impact the performance of a program?

Answer 77

Compiler optimization flags can significantly impact performance by influencing how the compiler generates machine code. Properly chosen flags can lead to faster execution, reduced code size, and improved utilization of hardware capabilities.

Question 78

Can the use of optimization flags introduce any risks or trade-offs?

Answer 78

Yes, there are potential risks, such as:
Aggressive optimizations may lead to subtle changes in program behavior.
Increased compilation time, especially at higher optimization levels.
Compatibility issues if code relies on specific behaviors not guaranteed under aggressive optimizations.

Question 79

Why is it important to carefully select optimization flags based on application needs?

Answer 79

Careful selection ensures a balance between improved performance and potential trade-offs. Understanding the impact of each flag on the program allows developers to tailor optimizations to specific requirements.

Question 80

Why is parallelizing the outer loop often preferred in performance engineering?

Answer 80

Parallelizing the outer loop is often preferred due to several reasons:

Data Decomposition: It allows for better data decomposition, distributing larger chunks of data among parallel threads, which can improve efficiency.

Reduced Synchronization Overhead: Parallelizing the outer loop can minimize the need for synchronization mechanisms between threads, reducing overhead and improving parallel scalability.

Cache Locality: Operating on contiguous chunks of data in the outer loop enhances cache locality, reducing cache misses and improving memory access efficiency.

Task Granularity: The outer loop usually represents a higher-level task, and parallelizing it provides a more coarse-grained approach, which can be advantageous in certain scenarios.

Improved Load Balancing: Parallelizing the outer loop often leads to better load balancing among threads, ensuring that each thread gets a similar amount of work.

In summary, parallelizing the outer loop offers benefits in terms of data distribution, synchronization, cache efficiency, task granularity, and load balancing, making it a preferred strategy in performance engineering.

Question 81

Why is reading and writing blocks of data important in performance optimization?

Answer 81

Reading and writing blocks of data are essential for optimizing cache utilization, enhancing spatial locality, reducing memory access overhead, and improving overall algorithm performance, especially in scenarios like matrix multiplication.

Question 82

How does tiling improve performance in matrix multiplication?

Answer 82

Tiling involves breaking down matrices into smaller blocks, allowing for better cache utilization and reducing memory access overhead. This optimization significantly improves performance, especially in algorithms like matrix multiplication, by enhancing spatial locality and minimizing cache misses.

Question 83

What does “fast math” refer to in compiler flags, and how does it impact performance?

Answer 83

“Fast math” is a compiler flag that allows reordering and optimizations of floating-point arithmetic for better performance. It sacrifices strict adherence to associativity rules, enabling the compiler to optimize expressions more aggressively. This can lead to improved vectorization and overall execution speed.

Question 84

In performance engineering, what strategy is employed for tiling matrices efficiently?

Answer 84

The strategy involves tiling matrices for every power of 2 simultaneously. This is achieved through recursive divide-and-conquer, where matrices are divided into 4 submatrices, solving subproblems of half the size, and performing matrix additions. It allows efficient use of caches and facilitates optimal performance tuning.

Question 85

Why is tiling for every power of 2 a practical approach in performance optimization?

Answer 85

Tiling for every power of 2 is practical because it enables efficient recursive divide-and-conquer algorithms. By dividing matrices into 4 submatrices and solving subproblems of half the size, the approach aligns well with binary representations, allowing simultaneous tiling for various power-of-2 sizes, enhancing cache utilization and computational efficiency.

Question 86

Running Java code on multiple cores

Answer 86

import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.*;
public class MultiCoreExample {
public static void main(String[] args) {
// Number of cores
int cores = Runtime.getRuntime().availableProcessors();
System.out.println(“Number of cores: “ + cores);
// Create ExecutorService with a thread pool
ExecutorService executorService = Executors.newFixedThreadPool(cores);
// List to store Future results
List<Future<Integer>> futures = new ArrayList<>();
// Define tasks
for (int i = 0; i < cores; i++) {
Callable<Integer> task = new MyTask();
Future<Integer> future = executorService.submit(task);
futures.add(future);
}
// Shutdown the executor service
executorService.shutdown();
// Collect results from tasks
int totalResult = 0;
for (Future<Integer> future : futures) {
try {
// Retrieve the result from each task
totalResult += future.get();
} catch (InterruptedException | ExecutionException e) {
e.printStackTrace();
}
}
System.out.println("Total result from all tasks: " + totalResult);
}
static class MyTask implements Callable<Integer> {
@Override
public Integer call() {
// Simulate some computation
int result = 0;
for (int i = 0; i < 1000000; i++) {
result += i;
}
return result;
}
}
}</Integer></Integer></Integer></Integer></Integer>

Question 87

What is AVX?

Answer 87

Advanced Vector Extensions (AVX) is a set of instructions for performing single instruction, multiple data (SIMD) operations, allowing parallel processing of multiple data elements.

Question 88

How does AVX contribute to performance in matrix multiplication?

Answer 88

AVX intrinsics enable vectorization, allowing simultaneous execution of operations on multiple data elements, improving the efficiency of matrix multiplication.

Question 89

What is the significance of AVX intrinsics in the context of performance engineering?

Answer 89

AVX intrinsics provide a substantial performance boost by leveraging vector hardware capabilities, achieving higher efficiency in numerical computations like matrix multiplication.

Question 90

What performance improvement is achieved using AVX intrinsics in the discussed example?

Answer 90

In the example, AVX intrinsics result in achieving 41% of peak performance, with a speedup of about 50,000 compared to the initial implementation.

Question 91

Why is AVX intrinsics usage limited in the example, and when do they surpass Intel MKL?

Answer 91

AVX intrinsics are limited by assuming power-of-2 matrices. Intel MKL surpasses this limitation by being more robust, making it better suited for various matrix sizes.

Question 92

How do AVX intrinsics contribute to overall performance improvements in numerical computations?

Answer 92

AVX intrinsics enhance vectorization, allowing operations on larger chunks of data simultaneously, resulting in substantial improvements in numerical computation performance.

Question 93

What are vectorization flags in compiler settings?

Answer 93

Vectorization flags are compiler directives that enable or control the use of vector instructions, such as SSE, AVX, or AVX2, to optimize code for parallel execution on vector hardware.

Question 94

Name some common vectorization flags used in compilers.

Answer 94

Common vectorization flags include -march=native, -mavx, -mavx2, and -mfma. These flags specify the target architecture and enable specific vector instruction sets.

Question 95

How does the -march=native flag contribute to vectorization?

Answer 95

The -march=native flag instructs the compiler to generate code optimized for the host machine’s architecture, leveraging the full capabilities of the vector hardware.

Question 96

What is the purpose of the -mfma flag in vectorization?

Answer 96

The -mfma flag enables the use of fused multiply-add (FMA) instructions, allowing the compiler to perform both multiplication and addition in a single instruction, enhancing vectorized performance.

Question 97

When might one use specific vectorization flags like -mavx or -mavx2?

Answer 97

Specific vectorization flags like -mavx or -mavx2 are used to explicitly target and enable particular vector instruction sets, providing fine-grained control over optimization.

Question 98

How can vectorization flags contribute to improved performance in numerical computations?

Answer 98

Vectorization flags optimize code for parallel execution on vector hardware, leading to increased efficiency in numerical computations by leveraging advanced instruction sets.

Question 99

What is parallel divide and conquer in the context of performance optimization?

Answer 99

Parallel divide and conquer is a strategy where a problem is recursively divided into subproblems, and multiple processors or cores are utilized to solve these subproblems concurrently, enhancing overall computational efficiency.

Question 100

How does parallel divide and conquer differ from the traditional divide and conquer approach?

Answer 100

In parallel divide and conquer, subproblems are solved concurrently using multiple processing units, whereas in traditional divide and conquer, subproblems are solved sequentially.

Question 101

What benefits does parallel divide and conquer offer in terms of performance improvement?

Answer 101

Parallel divide and conquer can significantly improve performance by leveraging parallelism, allowing multiple tasks to be executed simultaneously, thereby reducing overall computation time.

Question 102

In what scenarios or types of problems is parallel divide and conquer particularly effective?

Answer 102

Parallel divide and conquer is particularly effective for problems that can be decomposed into independent subproblems, enabling efficient parallel execution without dependencies among the subtasks.

Question 103

What role does recursion play in the parallel divide and conquer strategy?

Answer 103

Recursion is used to break down the original problem into smaller, more manageable subproblems. Each subproblem is then solved independently, and the results are combined to obtain the solution to the original problem.

Question 104

How can developers implement parallel divide and conquer in programming languages supporting parallelism?

Answer 104

Developers can use parallel programming constructs or frameworks, such as OpenMP or MPI, to implement parallel divide and conquer. These tools provide mechanisms to distribute subproblems across multiple processors or cores.

Question 105

What are the potential challenges or considerations when implementing parallel divide and conquer?

Answer 105

Developers need to carefully manage synchronization, load balancing, and communication overhead among parallel tasks to ensure optimal performance. Efficient distribution of subproblems is crucial for achieving good parallel scalability.